Global Inland Capture and Culture Finfisheries Follow Different Trends When Evaluated by the Human Development Index
Bibliographic record
Abstract
To assess whether and how socioeconomic factors might be influencing global freshwater finfisheries, inland fishery data reported to the FAO between 1950 and 2015 were grouped by capture and culture, country human development index, plotted, and compared. We found that while capture inland finfishes have greatly increased on a global scale, this trend is being driven almost entirely by poorly developed (Tier-3) countries which also identify only 17% of their catch. In contrast, capture finfisheries have recently plateaued in moderately-developed (Tier-2) countries which are also identifying 16% of their catch but are dominated by a single country, China. In contrast, reported capture finfisheries are declining in well-developed (Tier-1) countries which identify nearly all (78%) of their fishes. Simultaneously, aquacultural activity has been increasing rapidly in both Tier-2 and Tier-3 countries, but only slowly in Tier-1 countries; remarkably, nearly all cultured species are being identified by all tier groups. These distinctly different trends suggest that socioeconomic factors influence how countries report and conduct capture finfisheries. Reported rapid increases in capture fisheries are worrisome in poorly developed countries because they cannot be explained and thus these fisheries cannot be managed meaningfully even though they depend on them for food. Our descriptive, proof-of-concept study suggests that socioeconomic factors should be considered in future, more sophisticated efforts to understand global freshwater fisheries which might include catch reconstruction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".