<scp>FISHGLOB</scp> : A collaborative infrastructure to bridge the gap between scientific monitoring and marine biodiversity conservation
Bibliographic record
Abstract
Abstract Large‐scale biodiversity assessments and conservation applications require integrated and up‐to‐date datasets across regions. In the oceans, monitoring is fragmented, which affects knowledge exchange and usage. Among existing monitoring programs, scientific bottom‐trawl surveys (SBTS) are long‐term, rich, and well‐maintained data sources at the scale of each sampled region, but these data are under‐utilized in biodiversity applications, especially across regions. This is hampered by the lack of an international community and database maintained through time. To address this, we created FISHGLOB, an infrastructure gathering SBTS and experts. In 5 years, we developed an integrated database of SBTS and a consortium gathering more than 100 experts and users. Here, we are sharing the project history, achievements, challenges, and outlooks. In particular, we reflect on the infrastructure‐building social and technical processes which will guide the development of similar infrastructures. The FISHGLOB project takes ocean monitoring one step forward in working as a unified community across disciplines and regions of the world.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.036 |
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".