Counting fish: a typology for fisheries catch data
Bibliographic record
Abstract
Good decisions ideally require good data. Here, we present a straightforward typology for the broad classification of fisheries catch data. At each stage in the reporting chain, from fisher to national/international agencies, fisheries catches can be: known and reported; known and underreported; unknown and overreported; or unknown and underreported. Here, we consider largely the data reporting at the national/international level. Unfortunately, experience has shown that scientists and managers often do not know or are unconcerned with which category their data falls within a country's complete data system, or how to deal with this problem, leading to considerable implications for management. Of these four categories, the underreporting of catches seems the likeliest and most common outcome, which inevitably leads to mismanagement and misallocations of fisheries resources. Attempts to improve catch data should be undertaken, particularly via the development of catch baselines through catch reconstructions and adoption of a transparent and comprehensive country-wide expansion approach. Such an approach not only helps address shifting baselines but identifies aspects of data improvement that can be implemented in future data collection. The taxonomy presented here is a conceptual first-order analytical tool to classify data status, and hence influence management decisions.
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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.031 | 0.035 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".