How much fish is being extracted from the oceans and what is it worth?
Bibliographic record
Abstract
Any analysis of the impacts of fishing on marine systems, as undertaken by the Sea Around Us project (www.seaaroundus.org), imposes critical demands on fine spatial data documenting the extraction of marine resources. Data sources such as those provided voluntarily from fishing countries through the Food and Agriculture Organization (FAO) of the United Nations are invaluable but have many limitations. Regional datasets are also important in that they provide better detail. Reconstruction of national datasets can also provide great insights into historical catch series (e.g., Zeller et al ., 2007), and are important to understand historic baselines (Jackson and Jacquet, this volume). These must be woven into one coherent and harmonized global dataset representing all extractions over time. To provide the necessary spatial detail, the global data are allocated to a fine grid of cells measuring just 30 by 30 minutes of latitude and longitude, resulting in over 180000 such cells covering the world's oceans. The taxonomic identity of the reported catch must be combined with comprehensive databases on where the species occur (and in what abundance) in order to complete this process. This spatial allocation must be further tempered by where countries fish, as not all coastal waters are available to all fleets. After considerable development by the Sea Around Us project, it is now possible to examine global catches and catch values in the necessary spatial context. Like detectives, we have been able to deduce who caught what, where, and when, and how much money they made in the process.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".