S75 | CyanoMetDB | Comprehensive database of secondary metabolites from cyanobacteria
Bibliographic record
Abstract
This is the collection associated with list S75 CyanoMetDB Comprehensive database of secondary metabolites from cyanobacteria on the NORMAN Suspect List Exchange. https://www.norman-network.com/nds/SLE/ CyanoMetDB is a comprehensive database of secondary metabolites from cyanobacteria manually curated from primary references described in Jones et al (2021), DOI: 10.1016/j.watres.2021.117017 (preprint DOI: 10.1101/2020.04.16.038703). This upload contains the 2023 release. Please cite Jones et al (2021) DOI: 10.1016/j.watres.2021.117017 and this record Janssen et al (2023) DOI: 10.5281/zenodo.7922070 when using this CyanoMetDB Version 2! Contents: CyanoMetDB XLSX database (2023 release): CyanoMetDB_v02_2023.xlsx Additional files for workflows: CSV format: CyanoMetDB_v02_2023.csv MetFrag local CSV file (original database abridged and reformatted for use in MetFrag): CyanoMetDB_v02_2023_MetFrag.csv Additional files for matching InChIKeys (rapid suspect flagging): CyanoMetDB_v02_2023_InChIKeys.txt
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.211 | 0.213 |
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".