Plain wording

stale · v2 provisional awaiting approval

What 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Out of date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

  1. 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

  2. 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

  3. 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

  4. 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

  5. Blood-flow efficiency across the capillary network rises slightly at the peak of optogenetic stimulation.

    slug capillary-efficiency-increases-4pctrole empiricalpanel fig9C

  6. 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

  7. 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

  8. 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)

  9. 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

  10. Activating cortical neurons drives a coordinated network-wide vascular response that single-vessel measurements cannot reveal.

    slug hypothesis-network-level-nvc-coordinationrole hypothesispanel hypothesis

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. The pipeline's radius estimator recovers simulated vessel radii well and stays stable as image noise rises.

    slug radius-estimation-r2-0p68role methodologicalpanel fig5

  18. 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

  19. 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

  20. 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

  21. 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)

  22. The UNETR ensemble traces vessel and neuron surfaces more accurately than ilastik, which over-segments vessels.

    slug unetr-outperforms-ilastik-hd95role controlpanel fig3

  23. 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

  24. 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

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.

scripts/plain_claims.pythe script that runs itnot in the repository

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.

extract/prompts/plain-claim.mdthe prompt it runs undernot in the repository

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.

  1. 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

  2. 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

PaperStateVersionLast runOutputCell
A three-dimensional immunofluorescence atlas of the …re-run after the runner changed; no cost recorded — these answers predate the fieldstalev22026-09-12artiushin-2026-spider-atlas.jsonjson
Distinct representational properties of cues and con…re-run after the runner changed; no cost recorded — these answers predate the fieldstalev22026-09-12bouyeure-2026-fear-rsa.jsonjson
Computational modelling identifies key determinants …re-run after the runner changed; no cost recorded — these answers predate the fieldstalev22026-09-12ejdrup-2026-dopamine.jsonjson
Contributions of insula and superior temporal sulcus…re-run after the runner changedstalev62026-09-12gadeke-2026-guilt-insula.jsonjson
Spatially targeted inhibitory rhythms differentially…re-run after the runner changedstalev32026-09-12headley-2026-inhibitory-rhythms.jsonjson
Feedback of peripheral saccade targets to early fove…re-run after the runner changed; one wording added for the promoted claimstalev22026-09-12kammer-2026-foveal-feedback.jsonjson
iGABASnFR2 is an improved genetically encoded protei…re-run after the runner changed; no cost recorded — these answers predate the fieldstalev32026-09-12kolb-2026-igabasnfr2.jsonjson
A deep learning pipeline for mapping in situ network…re-run after the runner changed; no cost recorded — these answers predate the fieldstalev22026-09-12rozak-2026-neurovascular-dl.jsonjson
Self-association enhances early attentional selectio…re-run after the runner changed; no cost recorded — these answers predate the fieldstalev22026-09-12scheller-2026-self-prioritization.jsonjson
Impaired excitability of fast-spiking neurons in a n…re-run after the runner changed; no cost recorded — these answers predate the fieldstalev22026-09-12wengert-2026-kcnc1.jsonjson

Inputs and outputs

Reads, besides its dependencies
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.