Plain wording
stale · v2 provisional awaiting approvalWhat does each claim say, in one sentence a non-specialist can read?
for A deep learning pipeline for mapping in situ network-level neurovascular coupling in multi-photon fluorescence microscopy · 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Out of date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/rozak-2026-neurovascular-dl/dl-model-scope-single-pipeline.md
What it produced24 claims
Read from site/src/data/plain-claims/rozak-2026-neurovascular-dl.json · 6 KB. model supplied:runs/rozak-2026-neurovascular-dl/plain-claim.answer.jsonprompt extract/prompts/plain-claim.md
-
Measuring a capillary's width at one point is enough to track how its volume changes, so full 3D segmentation is unnecessary.
slug alt-point-measurement-estimates-vessel-volumerole hypothesis
-
Under weak blue-light stimulation an artery and a capillary dilated while a venule did not change.
slug artery-dilates-venule-unchanged-at-low-powerrole empiricalpanel fig7A
-
A capillary's radius varies substantially along its own length even at rest, so a single-point width cannot stand for its volume.
slug baseline-intra-vessel-radius-varies-24pctrole empiricalpanel fig7
-
Capillary dilations after blue light that activates ChR2 are larger than those after green control light.
slug blue-light-dilations-exceed-green-controlrole controlpanel fig8A
-
Blood-flow efficiency across the capillary network rises slightly at the peak of optogenetic stimulation.
slug capillary-efficiency-increases-4pctrole empiricalpanel fig9C
-
Capillaries that constrict after stimulation sit deeper in cortex than those that dilate, which cluster nearer the surface.
slug constrictions-deeper-than-dilationsrole empiricalpanel fig8E
-
Capillaries that dilate after ChR2 activation lie closer to labelled pyramidal neurons than ones that constrict, with no such split under control light.
slug dilations-nearer-neurons-than-constrictionsrole empiricalpanel fig8D
-
Every neurovascular measurement comes from one deep-learning pipeline trained on a single mouse preparation and needing a GPU.
slug dl-model-scope-single-pipelinerole scopepanel fig1 (architecture)
-
A deep-learning segmentation and graph pipeline can measure neurovascular coupling automatically across hundreds of connected vessels at once.
slug hypothesis-dl-pipeline-enables-network-nvcrole hypothesispanel hypothesis
-
Activating cortical neurons drives a coordinated network-wide vascular response that single-vessel measurements cannot reveal.
slug hypothesis-network-level-nvc-coordinationrole hypothesispanel hypothesis
-
During strong optogenetic stimulation highly connected vessels respond together with other highly connected vessels more than at rest.
slug network-assortativity-increases-stimulationrole empiricalpanel fig9B
-
The NOVAS3D segmentation model gives plausible vessel outlines on another mouse strain, on rat, and on light-sheet images, without retraining.
slug novas3d-generalizes-qualitatively-oodrole empiricalpanel app1fig12, app1fig13
-
NOVAS3D segments vessels in three dimensions more accurately than the ilastik classifier on the deposited two-photon test images.
slug novas3d-outperforms-ilastikrole controlpanel fig3, fig4
-
NOVAS3D's accuracy is measured only in Thy1-ChR2-YFP mice under one imaging protocol, and in no other strain, species or modality.
slug novas3d-single-preparation-scoperole scopepanel fig1 (architecture); methods section
-
The pipeline should beat ilastik at segmentation, find more vessel segments than any single time point, and recover simulated radii accurately.
slug prediction-pipeline-outperforms-baselinesrole predictionpanel prediction
-
Blue-light stimulation should separate dilations from constrictions by distance from neurons and shift network-wide flow measures, unlike control light.
slug prediction-pipeline-reveals-network-coordinationrole predictionpanel prediction
-
The pipeline's radius estimator recovers simulated vessel radii well and stays stable as image noise rises.
slug radius-estimation-r2-0p68role methodologicalpanel fig5
-
Aligning the volumes across time points and merging their masks nearly doubles the number of vessel segments found per field of view.
slug registration-doubles-vessel-countrole methodological
-
A vessel counts as responding if its radius change exceeds twice its baseline variability, a threshold varied only in an appendix.
slug responder-threshold-2sd-untestedrole scopepanel app1fig14
-
The pipeline is benchmarked and applied in one anaesthetised mouse preparation, with generalisation to others shown only by eye.
slug scope-pipeline-and-application-paperrole scopepanel scope
-
Measuring vessels one at a time cannot predict how blood flow is redistributed across the cortical microvascular network.
slug synthesis-individual-vessel-measurements-insufficientrole synthesispanel synthesis (Discussion)
-
The UNETR ensemble traces vessel and neuron surfaces more accurately than ilastik, which over-segments vessels.
slug unetr-outperforms-ilastik-hd95role controlpanel fig3
-
Vessel radius changes during stimulation vary widely, and dilating vessels lie closer to active neurons than constricting ones.
slug vessel-radius-heterogeneity-stimulationrole empiricalpanel fig6
-
In wild-type mice without ChR2, blue and green light produce no difference in capillary radius.
slug wt-controls-no-blue-green-differencerole controlpanel app1fig9
| # | slug | role | panel | plain |
|---|---|---|---|---|
| 1 | alt-point-measurement-estimates-vessel-volume | hypothesis | Measuring a capillary's width at one point is enough to track how its volume changes, so full 3D segmentation is unnecessary. | |
| 2 | artery-dilates-venule-unchanged-at-low-power | empirical | fig7A | Under weak blue-light stimulation an artery and a capillary dilated while a venule did not change. |
| 3 | baseline-intra-vessel-radius-varies-24pct | empirical | fig7 | A capillary's radius varies substantially along its own length even at rest, so a single-point width cannot stand for its volume. |
| 4 | blue-light-dilations-exceed-green-control | control | fig8A | Capillary dilations after blue light that activates ChR2 are larger than those after green control light. |
| 5 | capillary-efficiency-increases-4pct | empirical | fig9C | Blood-flow efficiency across the capillary network rises slightly at the peak of optogenetic stimulation. |
| 6 | constrictions-deeper-than-dilations | empirical | fig8E | Capillaries that constrict after stimulation sit deeper in cortex than those that dilate, which cluster nearer the surface. |
| 7 | dilations-nearer-neurons-than-constrictions | empirical | fig8D | Capillaries that dilate after ChR2 activation lie closer to labelled pyramidal neurons than ones that constrict, with no such split under control light. |
| 8 | dl-model-scope-single-pipeline | scope | fig1 (architecture) | Every neurovascular measurement comes from one deep-learning pipeline trained on a single mouse preparation and needing a GPU. |
| 9 | hypothesis-dl-pipeline-enables-network-nvc | hypothesis | hypothesis | A deep-learning segmentation and graph pipeline can measure neurovascular coupling automatically across hundreds of connected vessels at once. |
| 10 | hypothesis-network-level-nvc-coordination | hypothesis | hypothesis | Activating cortical neurons drives a coordinated network-wide vascular response that single-vessel measurements cannot reveal. |
| 11 | network-assortativity-increases-stimulation | empirical | fig9B | During strong optogenetic stimulation highly connected vessels respond together with other highly connected vessels more than at rest. |
| 12 | novas3d-generalizes-qualitatively-ood | empirical | app1fig12, app1fig13 | The NOVAS3D segmentation model gives plausible vessel outlines on another mouse strain, on rat, and on light-sheet images, without retraining. |
| 13 | novas3d-outperforms-ilastik | control | fig3, fig4 | NOVAS3D segments vessels in three dimensions more accurately than the ilastik classifier on the deposited two-photon test images. |
| 14 | novas3d-single-preparation-scope | scope | fig1 (architecture); methods section | NOVAS3D's accuracy is measured only in Thy1-ChR2-YFP mice under one imaging protocol, and in no other strain, species or modality. |
| 15 | prediction-pipeline-outperforms-baselines | prediction | prediction | The pipeline should beat ilastik at segmentation, find more vessel segments than any single time point, and recover simulated radii accurately. |
| 16 | prediction-pipeline-reveals-network-coordination | prediction | prediction | Blue-light stimulation should separate dilations from constrictions by distance from neurons and shift network-wide flow measures, unlike control light. |
| 17 | radius-estimation-r2-0p68 | methodological | fig5 | The pipeline's radius estimator recovers simulated vessel radii well and stays stable as image noise rises. |
| 18 | registration-doubles-vessel-count | methodological | Aligning the volumes across time points and merging their masks nearly doubles the number of vessel segments found per field of view. | |
| 19 | responder-threshold-2sd-untested | scope | app1fig14 | A vessel counts as responding if its radius change exceeds twice its baseline variability, a threshold varied only in an appendix. |
| 20 | scope-pipeline-and-application-paper | scope | scope | The pipeline is benchmarked and applied in one anaesthetised mouse preparation, with generalisation to others shown only by eye. |
| 21 | synthesis-individual-vessel-measurements-insufficient | synthesis | synthesis (Discussion) | Measuring vessels one at a time cannot predict how blood flow is redistributed across the cortical microvascular network. |
| 22 | unetr-outperforms-ilastik-hd95 | control | fig3 | The UNETR ensemble traces vessel and neuron surfaces more accurately than ilastik, which over-segments vessels. |
| 23 | vessel-radius-heterogeneity-stimulation | empirical | fig6 | Vessel radius changes during stimulation vary widely, and dilating vessels lie closer to active neurons than constricting ones. |
| 24 | wt-controls-no-blue-green-difference | control | app1fig9 | In wild-type mice without ChR2, blue and green light produce no difference in capillary radius. |
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.
-
v2 · 2026-09-12 · supplied:runs/rozak-2026-neurovascular-dl/plain-claim.answer.json
re-run after the runner changed; no cost recorded — these answers predate the field
python3 scripts/plain_claims.py rozak-2026-neurovascular-dl --answer runs/rozak-2026-neurovascular-dl/plain-claim.answer.json
-
v1 · 2026-09-11 · supplied:runs/rozak-2026-neurovascular-dl/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 rozak-2026-neurovascular-dl --answer runs/rozak-2026-neurovascular-dl/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
-
- 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
-
- site/src/data/plain-claims/{paper}.json
One per paper — the table above links each one that exists.
- Views
-
- 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.