Organizing and disseminating marine biodiversity information: the FishBase and SeaLifeBase story
Bibliographic record
Abstract
INTRODUCTION In the 1970s, data manipulation in fisheries science relied on paper and pencil methods aided by programmable calculators and, in some sophisticated laboratories, on huge computer systems with punch cards (see Munro, this volume). Thus, assembling large amounts of data was limited by the availability of paper copies of peer-reviewed publications, the “reprints” of lore, and grey literature. This was the environment in which Daniel Pauly found himself, struggling with how he could test his hypothesis on the relationship between gill size and the growth of fishes (Pauly, 2010; Bakun, this volume; Cheung, this volume). Testing such a hypothesis needed a large amount of empirical data, which might be available in principle, but if so, not at one's fingertips. Inspired by Walter Fischer's work on the Food and Agriculture Organization of the United Nations (FAO) species identification sheets in the mid-1970s, Daniel believed that assembling data from already published literature was essential for a timely response to the needs of fisheries management, which, at the time, used analytical models requiring growth and mortality estimates (Munro, this volume). And index cards, he found, were ideal for recording the specific data required for assessing size at age, maximum sizes and ages, and growth and natural mortality parameter estimates, as well as temperature and other environmental variables and their sources. The index card collection provided data for his widely used compilation of length–growth parameters (Pauly, 1978), which served as a basis for investigating the role of gills in fish growth in his doctoral thesis (Pauly, 1979) and subsequent papers, and for his now classic paper on natural mortality (Pauly, 1980).
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.039 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.021 |
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".