Introduction to the shared near infrared spectroscopy format
Bibliographic record
Abstract
Significance: Functional near-infrared spectroscopy (fNIRS) is a popular neuroimaging technique with proliferating hardware platforms, analysis approaches, and software tools. There has not been a standardized file format for storing fNIRS data, which has hindered the sharing of data as well as the adoption and development of software tools. Aim: We endeavored to design a file format to facilitate the analysis and sharing of fNIRS data that is flexible enough to meet the community's needs and sufficiently defined to be implemented consistently across various hardware and software platforms. Approach: The shared NIRS format (SNIRF) specification was developed in consultation with the academic and commercial fNIRS community and the Society for functional Near Infrared Spectroscopy. Results: The SNIRF specification defines a format for fNIRS data acquired using continuous wave, frequency domain, time domain, and diffuse correlation spectroscopy devices. Conclusions: We present the SNIRF along with validation software and example datasets. Support for reading and writing SNIRF data has been implemented by major hardware and software platforms, and the format has found widespread use in the fNIRS community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.040 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".