Construction and first applications of a global cost of fishing database
Bibliographic record
Abstract
Abstract Lam, V. W. Y., Sumaila, U. R., Dyck, A., Pauly, D., and Watson, R. 2011. Construction and first applications of a global cost of fishing database. – ICES Journal of Marine Science, 68: 1996–2004. The development of a new global database of fishing cost is described, and an overview of fishing cost patterns at national, regional, and global scales is provided. This fishing cost database provides economic information required for assessing the economics of fisheries at various scales. It covers variable and fixed costs of maritime countries, representing ∼98% of global landings in 2005. Linked to country and gear-type combinations, cost estimates can be mapped to a database of spatially allocated fisheries catches for future analysis in both spatial and temporal dimensions. The global average variable cost per tonne of catch in 2005 is estimated to range between US$639 and $1217, and the total cost per tonne from $763 to $1477, with mean values of $928 and $1120, respectively. The total global variable fishing cost is estimated to be in the range US$50–96 billion per year, with a mean of $73 billion per annum in 2005 dollar equivalents.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".