Abstract ↔ claims
run not observed · v1 provisional awaiting approvalWhich claims does the abstract carry, and which does it drop?
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.
What this layer produced
Abstract mapped to claims
The paper's abstract is shown with each sentence linked to the claim(s) it represents in the dependency graph. Hover or click a sentence to highlight the corresponding claim cards. Below: what the graph contains that the abstract leaves out, and vice versa.
1Striatal dopamine (DA) release regulates reward-related learning and motivation and is believed to consist of a short-lived phasic and continuous tonic component. 2Here, we build a large-scale three-dimensional model of extracellular DA dynamics in dorsal (DS) and ventral striatum (VS). 3The model predicts rapid dynamics in DS with little to no basal DA and slower dynamics in the VS enabling build-up of tonic DA levels. 4These regional differences do not reflect release-related phenomena but rather differential dopamine transporter (DAT) activity. 5Interestingly, our simulations posit DAT nanoclustering as a possible regulator of this activity. 6Receptor binding simulations show that D1 receptor occupancy follows extracellular DA concentration with milliseconds delay, while D2 receptors do not respond to brief pauses in firing but rather integrate DA signal over seconds. 7Summarised, our model distills recent experimental observations into a computational framework that challenges prevailing paradigms of striatal DA signalling.
- ds-vs-vmax-ratio-assumed fig2A (implied throughout) The 3:1 DS:VS DAT Vmax ratio (DS = 6 µM·s⁻¹, VS = 2 µM·s⁻¹) is assumed from published literature rather than directly measured in this study; the immunostaining gradient (Figure 2—supplement 1) corroborates this assumption at the protein level but does not directly establish the functional Vmax ratio.
- M1 d2r-initialization-unjustified fig1H D2 receptor occupancy is initialized at 0.4 in all receptor dynamics simulations without derivation from steady state; at the modeled EC50 of 7 nM and simulated tonic [DA] of ~10 nM in DS, equilibrium occupancy would be approximately 0.59. No sensitivity analysis over this initialization is reported.
- Sc2 nanoclustering-model-varicosity-scale fig4C, fig4D, fig4E, fig4F The nanoclustering simulations (Figure 4C–F) operate in a standalone varicosity-scale model (1.8 × 1.8 µm domain, 0.02 µm voxels) architecturally separate from the tissue-level model used in Figures 1–3 (100 µm domain, 1 µm voxels); no formal coupling exists between the two models and no effective-Vmax output from the nanoclustering simulation feeds into the tissue simulation.
- Sc1 nanoclustering-constant-vmax-constraint fig4C, fig4D, fig4E, fig4F The nanoclustering simulations hold total DAT Vmax constant across clustered and unclustered conditions — the per-voxel rate is multiplied by a normalization factor so total integrated uptake capacity is identical; if DAT nanoclustering co-occurs with increased total DAT expression in biology, the clearance-slowing result would not hold.
- E3 low-burst-no-spillover-high-burst-does fig1G 3 APs at 10 Hz generates no significant DA spillover outside the burst zone; 6 APs at 20 Hz and 12 APs at 40 Hz cause frequency-dependent spillover exposing 10× and 30× the burst volume to concentrations above 100 nM respectively.
- E1 d2r-occupancy-higher-in-vs fig2G D2R occupancy during pacemaker activity is approximately 0.8 in VS versus approximately 0.55 in DS, consistent with higher prevailing tonic DA in VS.
- E4 vs-lowest-percentiles-above-10nm fig2D Even the lowest DA concentration percentiles in VS exceed 10 nM during 4 Hz pacemaker activity.
- H3.2 fscv-matches-may-wightman-1989 fig2E Simulated FSCV responses to 10, 30, and 60 Hz stimulation closely replicate May & Wightman (1989): VS reaches considerably higher peak DA than DS at all three stimulation frequencies.
- H3.3 vmax-modulation-larger-impact-in-vs fig3K A ±50% change in DAT Vmax shifts tonic DA by 38 nM in VS but only 11 nM in DS, indicating VS operates closer to the Km saturation regime and is more sensitive to DAT modulation.
- H3.5 vs-low-active-fraction-resembles-ds-distribution fig3B VS at 5% active terminals produces a spatial DA distribution resembling DS at 100% active terminals, demonstrating VS operates in a low-focality high-coverage regime while DS requires dense terminal participation for equivalent spatial reach.
- E2 dat-immunostaining-dorsoventral-gradient fig2—supplement 1B, fig2—supplement 1C DAT expression is significantly higher in dorsal than ventral striatum (p=0.0021, one-sided t-test, n=4 mice), corroborating the 3:1 Vmax ratio assumed in the model.
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.
Artifacts
Versions
From the run ledger. There is no changelog beside it to keep in step.
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v1 · 2026-04-20 · unrecorded backfilled from the artifact
backfilled from the artifact on disk
This layer across the corpus
Across the corpus
9 run not observed · 1 stale·a paper links to its own cell, where this layer's output for it is rendered
Inputs and outputs
- Reads, besides its dependencies
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- extract/prompts/abstract-map.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/abstract-mapping/{paper}.json
One per paper — the table above links each one that exists.
- Views
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- document — rendered above, from the artifact itself
- comparison — on the cell page, two versions aligned by the matcher, wherever the ledger holds more than one
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> abstract-map
Underneath, that runs cd extract && python3 -m claim_graphs.cli abstract-map --paper {paper}.