An Improved Reconstruction of Total Marine Fisheries Catches for the New Hebrides and the Republic of Vanuatu, 1950–2014
Bibliographic record
Abstract
1. Abstract For many small island nations, fisheries provide residents with both food security and economic stability. However, in order to create effective and sustainable fisheries policies and management that will ensure a growing population can prosper, policy makers need to know what is being fished and how much is fished. Vanuatu, the smallest country in Melanesia, has a declared and claimed Exclusive Economic Zone (EEZ) of over 820,000 km2 and fisheries resources play a large part in the food security and economic stability of this country. This reconstruction of total marine fisheries catch of Vanuatu for 1950 to 2014 faced major data gaps. It showed that the reconstructed total catches of nearly 1.4 million t were 40% higher than the 977,997 t reported by the Food and Agriculture Organization (FAO) on behalf of Vanuatu for the same period. However, if large-scale industrial catches are excluded, the reconstructed small-scale fisheries catches (~ 270,000 t) were over 200% higher than the 114,862 t of reported catch that were assumed to represent the small-scale sector in FAO data. Subsistence catches made up almost 93% of small-scale catches, followed by artisanal and recreational catches with ~7% and less than 1%, respectively. By continuously improving the fisheries data of Vanuatu for both the past and the present, policy makers, stakeholders and fishers can make better decisions that will maintain the benefits of marine fishery resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".