Plain wording
stale · v2 provisional awaiting approvalWhat does each claim say, in one sentence a non-specialist can read?
for Computational modelling identifies key determinants of subregion-specific dopamine dynamics in the striatum · this layer across all papers · json
Provisional
This layer needs a corpus-scope decision that has not been ruled on yet, so what it produces would change if the decision changed. It waits on claim-format, relation-vocab.
Awaiting approval
Waiting for approval. That is a statement about the record, not about whether anyone has read this: people read the corpus without stamping what they read, and only a stamp leaves a trace. Approval is an operation on a version, not a step of its own — it is recorded against the version it was granted to, so running this layer again does not carry it forward.
Out of date
These inputs changed after this ran:
- claims/ejdrup-2026-dopamine/d1r-tracks-da-50ms-delay.md
- claims/ejdrup-2026-dopamine/d2r-initialization-unjustified.md
- claims/ejdrup-2026-dopamine/d2r-insensitive-to-brief-pauses.md
- claims/ejdrup-2026-dopamine/d2r-integrates-over-seconds.md
- claims/ejdrup-2026-dopamine/d2r-occupancy-higher-in-vs.md
- claims/ejdrup-2026-dopamine/dat-clustering-greater-in-vs.md
- claims/ejdrup-2026-dopamine/dat-immunostaining-dorsoventral-gradient.md
- claims/ejdrup-2026-dopamine/dat-nanoclustering-slows-clearance.md
- claims/ejdrup-2026-dopamine/ds-lacks-pervasive-tonic-da.md
- claims/ejdrup-2026-dopamine/ds-vs-vmax-ratio-assumed.md
- claims/ejdrup-2026-dopamine/fscv-matches-may-wightman-1989.md
- claims/ejdrup-2026-dopamine/hypothesis-d1-d2-temporal-distinction.md
- claims/ejdrup-2026-dopamine/hypothesis-nanoclustering-regulates-vmax.md
- claims/ejdrup-2026-dopamine/hypothesis-vmax-explains-regional-difference.md
- claims/ejdrup-2026-dopamine/interprets-cragg-rice-vmax-ratio.md
- claims/ejdrup-2026-dopamine/interprets-may-wightman-1989-fscv.md
- claims/ejdrup-2026-dopamine/low-burst-no-spillover-high-burst-does.md
- claims/ejdrup-2026-dopamine/nanoclustering-constant-vmax-constraint.md
- claims/ejdrup-2026-dopamine/nanoclustering-model-varicosity-scale.md
- claims/ejdrup-2026-dopamine/vmat2-gradient-absent.md
- claims/ejdrup-2026-dopamine/vmax-modulation-larger-impact-in-vs.md
- claims/ejdrup-2026-dopamine/vmax-only-parameter-driving-regional-difference.md
- claims/ejdrup-2026-dopamine/vs-low-active-fraction-resembles-ds-distribution.md
- claims/ejdrup-2026-dopamine/vs-lowest-percentiles-above-10nm.md
- claims/ejdrup-2026-dopamine/vs-maintains-pervasive-tonic-da.md
What it produced25 claims
Read from site/src/data/plain-claims/ejdrup-2026-dopamine.json · 6 KB. model supplied:runs/ejdrup-2026-dopamine/plain-claim.answer.jsonprompt extract/prompts/plain-claim.md
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D1 receptor occupancy trails extracellular dopamine by about 50 milliseconds and rises only during bursts, not tonic firing.
slug d1r-tracks-da-50ms-delayrole empiricalpanel fig1H, fig1I
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Every simulation starts D2 occupancy at a value the model's own parameters do not predict, with no test of how much it matters.
slug d2r-initialization-unjustifiedrole methodologicalpanel fig1H
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A full one-second pause in firing barely lowers D2 receptor occupancy, across a tenfold range of assumed D2 affinity.
slug d2r-insensitive-to-brief-pausesrole empiricalpanel fig1J
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D2 receptor occupancy takes seconds to fall back after a burst, so it cannot separate closely spaced bursts.
slug d2r-integrates-over-secondsrole empiricalpanel fig1H
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During pacemaker firing, D2 receptor occupancy is higher in ventral than dorsal striatum, matching the higher tonic dopamine.
slug d2r-occupancy-higher-in-vsrole empiricalpanel fig2G
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Super-resolution imaging shows the dopamine transporter is more nanoclustered in ventral than in dorsal striatum.
slug dat-clustering-greater-in-vsrole empiricalpanel fig4M, fig4N
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Dopamine transporter protein is more abundant in dorsal than ventral striatum, matching the uptake-rate ratio the model assumes.
slug dat-immunostaining-dorsoventral-gradientrole empiricalpanel fig2—supplement 1B, fig2—supplement 1C
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Dense transporter nanoclusters clear dopamine about twice as slowly as evenly spread ones, because dopamine runs out around them.
slug dat-nanoclustering-slows-clearancerole empiricalpanel fig4C, fig4D, fig4E, fig4F, fig4G
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During pacemaker firing, dorsal striatum holds separate dopamine hotspots with much of the volume empty, not a tonic baseline.
slug ds-lacks-pervasive-tonic-darole empiricalpanel fig1D, fig1E, fig1F
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The three-to-one dorsal-to-ventral ratio of transporter uptake capacity is taken from prior literature, not measured here.
slug ds-vs-vmax-ratio-assumedrole scopepanel fig2A (implied throughout)
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Simulated voltammetry reproduces published recordings where ventral striatum peaks higher than dorsal at every frequency.
slug fscv-matches-may-wightman-1989role empiricalpanel fig2E
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D1 receptors detect brief dopamine bursts while D2 receptors integrate dopamine over seconds, because their kinetics differ.
slug hypothesis-d1-d2-temporal-distinctionrole hypothesispanel hypothesis
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Packing transporters into dense nanoclusters lowers their effective uptake capacity even with total transporter unchanged.
slug hypothesis-nanoclustering-regulates-vmaxrole hypothesispanel hypothesis
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Dorsal hotspots versus ventral tonic coverage follow from the difference in uptake capacity, not from how dopamine is released.
slug hypothesis-vmax-explains-regional-differencerole hypothesispanel hypothesis
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Earlier voltammetry work established that dopamine uptake capacity is roughly three times higher in dorsal than ventral striatum.
slug interprets-cragg-rice-vmax-ratiorole literature-contextpanel fig2A (implied parameterization)
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May and Wightman found in rat slices that ventral striatum reaches higher evoked dopamine peaks than dorsal at every frequency.
slug interprets-may-wightman-1989-fscvrole literature-contextpanel fig2E (explicit replication comparison)
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Short low-frequency bursts keep dopamine inside the burst zone, while stronger bursts spill it into a far larger volume.
slug low-burst-no-spillover-high-burst-doesrole empiricalpanel fig1G
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The nanoclustering simulations hold total uptake capacity fixed, so slower clearance would not follow if clustering added DAT.
slug nanoclustering-constant-vmax-constraintrole scopepanel fig4C, fig4D, fig4E, fig4F
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The nanoclustering work uses a separate tiny-scale model never coupled back to the tissue-scale model of the other figures.
slug nanoclustering-model-varicosity-scalerole scopepanel fig4C, fig4D, fig4E, fig4F
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VMAT2, which loads dopamine into vesicles, shows no dorsoventral gradient, so release capacity cannot explain the difference.
slug vmat2-gradient-absentrole controlpanel fig2—supplement 1A, fig2—supplement 1B, fig2—supplement 1C
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Ventral striatum, running closer to transporter saturation, shifts its tonic dopamine far more than dorsal when uptake changes.
slug vmax-modulation-larger-impact-in-vsrole empiricalpanel fig3K
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Of all the parameters swept, only uptake capacity affects dorsal and ventral striatum differently, the rest moving both alike.
slug vmax-only-parameter-driving-regional-differencerole empiricalpanel fig3B, fig3C, fig3E, fig3F, fig3G, fig3K, fig3L
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Ventral striatum with a twentieth of its terminals active spreads dopamine as widely as dorsal striatum with all of them active.
slug vs-low-active-fraction-resembles-ds-distributionrole empiricalpanel fig3B
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Even the lowest-concentration parts of ventral striatum stay above ten nanomolar dopamine during pacemaker firing.
slug vs-lowest-percentiles-above-10nmrole empiricalpanel fig2D
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With its lower uptake capacity, ventral striatum fills the whole simulated volume with diffuse tonic dopamine, not hotspots.
slug vs-maintains-pervasive-tonic-darole empiricalpanel fig2A, fig2B, fig2C
| # | slug | role | panel | plain |
|---|---|---|---|---|
| 1 | d1r-tracks-da-50ms-delay | empirical | fig1H, fig1I | D1 receptor occupancy trails extracellular dopamine by about 50 milliseconds and rises only during bursts, not tonic firing. |
| 2 | d2r-initialization-unjustified | methodological | fig1H | Every simulation starts D2 occupancy at a value the model's own parameters do not predict, with no test of how much it matters. |
| 3 | d2r-insensitive-to-brief-pauses | empirical | fig1J | A full one-second pause in firing barely lowers D2 receptor occupancy, across a tenfold range of assumed D2 affinity. |
| 4 | d2r-integrates-over-seconds | empirical | fig1H | D2 receptor occupancy takes seconds to fall back after a burst, so it cannot separate closely spaced bursts. |
| 5 | d2r-occupancy-higher-in-vs | empirical | fig2G | During pacemaker firing, D2 receptor occupancy is higher in ventral than dorsal striatum, matching the higher tonic dopamine. |
| 6 | dat-clustering-greater-in-vs | empirical | fig4M, fig4N | Super-resolution imaging shows the dopamine transporter is more nanoclustered in ventral than in dorsal striatum. |
| 7 | dat-immunostaining-dorsoventral-gradient | empirical | fig2—supplement 1B, fig2—supplement 1C | Dopamine transporter protein is more abundant in dorsal than ventral striatum, matching the uptake-rate ratio the model assumes. |
| 8 | dat-nanoclustering-slows-clearance | empirical | fig4C, fig4D, fig4E, fig4F, fig4G | Dense transporter nanoclusters clear dopamine about twice as slowly as evenly spread ones, because dopamine runs out around them. |
| 9 | ds-lacks-pervasive-tonic-da | empirical | fig1D, fig1E, fig1F | During pacemaker firing, dorsal striatum holds separate dopamine hotspots with much of the volume empty, not a tonic baseline. |
| 10 | ds-vs-vmax-ratio-assumed | scope | fig2A (implied throughout) | The three-to-one dorsal-to-ventral ratio of transporter uptake capacity is taken from prior literature, not measured here. |
| 11 | fscv-matches-may-wightman-1989 | empirical | fig2E | Simulated voltammetry reproduces published recordings where ventral striatum peaks higher than dorsal at every frequency. |
| 12 | hypothesis-d1-d2-temporal-distinction | hypothesis | hypothesis | D1 receptors detect brief dopamine bursts while D2 receptors integrate dopamine over seconds, because their kinetics differ. |
| 13 | hypothesis-nanoclustering-regulates-vmax | hypothesis | hypothesis | Packing transporters into dense nanoclusters lowers their effective uptake capacity even with total transporter unchanged. |
| 14 | hypothesis-vmax-explains-regional-difference | hypothesis | hypothesis | Dorsal hotspots versus ventral tonic coverage follow from the difference in uptake capacity, not from how dopamine is released. |
| 15 | interprets-cragg-rice-vmax-ratio | literature-context | fig2A (implied parameterization) | Earlier voltammetry work established that dopamine uptake capacity is roughly three times higher in dorsal than ventral striatum. |
| 16 | interprets-may-wightman-1989-fscv | literature-context | fig2E (explicit replication comparison) | May and Wightman found in rat slices that ventral striatum reaches higher evoked dopamine peaks than dorsal at every frequency. |
| 17 | low-burst-no-spillover-high-burst-does | empirical | fig1G | Short low-frequency bursts keep dopamine inside the burst zone, while stronger bursts spill it into a far larger volume. |
| 18 | nanoclustering-constant-vmax-constraint | scope | fig4C, fig4D, fig4E, fig4F | The nanoclustering simulations hold total uptake capacity fixed, so slower clearance would not follow if clustering added DAT. |
| 19 | nanoclustering-model-varicosity-scale | scope | fig4C, fig4D, fig4E, fig4F | The nanoclustering work uses a separate tiny-scale model never coupled back to the tissue-scale model of the other figures. |
| 20 | vmat2-gradient-absent | control | fig2—supplement 1A, fig2—supplement 1B, fig2—supplement 1C | VMAT2, which loads dopamine into vesicles, shows no dorsoventral gradient, so release capacity cannot explain the difference. |
| 21 | vmax-modulation-larger-impact-in-vs | empirical | fig3K | Ventral striatum, running closer to transporter saturation, shifts its tonic dopamine far more than dorsal when uptake changes. |
| 22 | vmax-only-parameter-driving-regional-difference | empirical | fig3B, fig3C, fig3E, fig3F, fig3G, fig3K, fig3L | Of all the parameters swept, only uptake capacity affects dorsal and ventral striatum differently, the rest moving both alike. |
| 23 | vs-low-active-fraction-resembles-ds-distribution | empirical | fig3B | Ventral striatum with a twentieth of its terminals active spreads dopamine as widely as dorsal striatum with all of them active. |
| 24 | vs-lowest-percentiles-above-10nm | empirical | fig2D | Even the lowest-concentration parts of ventral striatum stay above ten nanomolar dopamine during pacemaker firing. |
| 25 | vs-maintains-pervasive-tonic-da | empirical | fig2A, fig2B, fig2C | With its lower uptake capacity, ventral striatum fills the whole simulated volume with diffuse tonic dopamine, not hotspots. |
How it is defined
A model answers this layer, so the prompt is the layer. It is reproduced below from the committed file, and it is a declared input — editing it makes every run that used it stale.
The declaration names this path and the repository does not have it. An input that does not exist hashes to nothing, so it cannot make a run stale — the layer is declared to depend on something it is not in fact tracking.
The declaration names this path and the repository does not have it. An input that does not exist hashes to nothing, so it cannot make a run stale — the layer is declared to depend on something it is not in fact tracking.
Artifacts
Versions
From the run ledger. There is no changelog beside it to keep in step.
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v2 · 2026-09-12 · supplied:runs/ejdrup-2026-dopamine/plain-claim.answer.json
re-run after the runner changed; no cost recorded — these answers predate the field
python3 scripts/plain_claims.py ejdrup-2026-dopamine --answer runs/ejdrup-2026-dopamine/plain-claim.answer.json
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v1 · 2026-09-11 · supplied:runs/ejdrup-2026-dopamine/plain-claim.answer.json
first run: one plain sentence per claim, answered by Claude Opus 5 through --dump-prompt and fed back through --answer
python3 scripts/plain_claims.py ejdrup-2026-dopamine --answer runs/ejdrup-2026-dopamine/plain-claim.answer.json
This layer across the corpus
Across the corpus
10 stale·a paper links to its own cell, where this layer's output for it is rendered
| Paper | State | Version | Last run | Output | Cell |
|---|---|---|---|---|---|
| A three-dimensional immunofluorescence atlas of the …re-run after the runner changed; no cost recorded — these answers predate the field | stale | v2 | 2026-09-12 | artiushin-2026-spider-atlas.json | json |
| Distinct representational properties of cues and con…re-run after the runner changed; no cost recorded — these answers predate the field | stale | v2 | 2026-09-12 | bouyeure-2026-fear-rsa.json | json |
| Computational modelling identifies key determinants …re-run after the runner changed; no cost recorded — these answers predate the field | stale | v2 | 2026-09-12 | ejdrup-2026-dopamine.json | json |
| Contributions of insula and superior temporal sulcus…re-run after the runner changed | stale | v6 | 2026-09-12 | gadeke-2026-guilt-insula.json | json |
| Spatially targeted inhibitory rhythms differentially…re-run after the runner changed | stale | v3 | 2026-09-12 | headley-2026-inhibitory-rhythms.json | json |
| Feedback of peripheral saccade targets to early fove…re-run after the runner changed; one wording added for the promoted claim | stale | v2 | 2026-09-12 | kammer-2026-foveal-feedback.json | json |
| iGABASnFR2 is an improved genetically encoded protei…re-run after the runner changed; no cost recorded — these answers predate the field | stale | v3 | 2026-09-12 | kolb-2026-igabasnfr2.json | json |
| A deep learning pipeline for mapping in situ network…re-run after the runner changed; no cost recorded — these answers predate the field | stale | v2 | 2026-09-12 | rozak-2026-neurovascular-dl.json | json |
| Self-association enhances early attentional selectio…re-run after the runner changed; no cost recorded — these answers predate the field | stale | v2 | 2026-09-12 | scheller-2026-self-prioritization.json | json |
| Impaired excitability of fast-spiking neurons in a n…re-run after the runner changed; no cost recorded — these answers predate the field | stale | v2 | 2026-09-12 | wengert-2026-kcnc1.json | json |
Inputs and outputs
- Reads, besides its dependencies
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- scripts/plain_claims.py · declared, and not in the repository — it hashes to nothing, so it cannot make a run stale
- extract/prompts/plain-claim.md · declared, and not in the repository — it hashes to nothing, so it cannot make a run stale
- Produces
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- site/src/data/plain-claims/{paper}.json
One per paper — the table above links each one that exists.
- Views
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- list — rendered above, over the 17 claims in the artifact
- table — rendered above, over the 17 claims in the artifact
Running it
The command comes from the declaration, so this text and what actually runs cannot
diverge. pipeline.py run also runs the unmet dependencies first.
python3 scripts/pipeline.py run <paper> plain-claim
Underneath, that runs python3 scripts/plain_claims.py {paper} --answer runs/{paper}/plain-claim.answer.json.