WU-BIMAC/NBOMicroscopyMetadataSpecs: 4DN-BINA-OME (NBO) Microscopy Metadata Specifications
Bibliographic record
Abstract
Note: v2.01 is a minor release that corrects minor errors that were found in v2.00. 4DN-BINA-OME (NBO) Microscopy Metadata Specifications v2.0 is the result of a proposal put forth by the 4D Nucleome (4DN) Imaging Standards Working Group and by the Bioimaging North America (BINA) Quality Control and Data Management Working Group (QC-DM-WG) for a suite of tiered Microscopy Metadata Specifications that extend the October 2016 version of the OME Data Model schema. v2.0 supersedes and replaces v1.07 of the guidelines, which were called Microscopy Metadata 4DN Guidelines. The next version of the NBO Microscopy Metadata Specifications will be released as part of a community outreach effort conducted in collaboration with the Quality Assessment and Reproducibility for Light Microscopy (QUAREP-LiMi) initiative. Development continues! Everyone is encouraged to contribute to the next release v02-10 draft of the specifications. Click here for a shortcut to the model directory. To create an issue, click on Issues, the "New Issue" button, and select whether you are submitting a proposed new feature/element or want to propose a change to elements of the current proposal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.201 | 0.293 |
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".