Fisheries in large marine ecosystems: Descriptions and diagnoses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a rationale for the description and diagnosis of fisheries at the level of Large Marine Ecosystems (LMEs), which is relatively new, and encompasses a series of concepts and indicators different from those typically used to describe fisheries at the stock level. We then document how catch data, which are usually available on a smaller scale, are mapped by the Sea Around Us Project (see www.seaaroundus.org) on a worldwide grid of half-degree lat.-long. cells. The time series of catches thus obtained for over 180,000 half-degree cells can be regrouped on any larger scale, here that of LMEs. This yields catch time series by species (groups) and LME, which began in 1950 when the FAO started collecting global fisheries statistics, and ends in 2004 with the last update of these datasets. The catch data by species, multiplied by ex-vessel price data and then summed, yield the value of the fishery for each LME, here presented as time series by higher (i.e., commercial) groups. Also, these catch data can be used to evaluate the primary production required (PPR) to sustain fisheries catches. PPR, when related to observed primary production, provides another index for assessing the impact of the countries fishing in LMEs. The mean trophic level of species caught by fisheries (or ‘Marine Trophic Index’) is also used, in conjunction with a related indicator, the Fishing-in-Balance Index (FiB), to assess changes in the species composition of the fisheries in LMEs. Also, newly conceived ‘Stock-Catch Status Plots’ are presented which document graphically, for each LME, both the increase in the number of stocks that moved from the fully exploited to the overexploited and collapsed stages, and the relative biomass of fish extracted from stocks in these various stages. Finally, original time series of estimated catch data are presented for the six LMEs of the coast of North Siberia, Arctic Alaska and Arctic Canada (all entirely contained within FAO Statistical Area 18), for which even crude catch estimates were previously unavailable. Altogether these descriptors of fisheries and ecosystem states over the last 50+ years allow a diagnosis of the fisheries of each LME, and inferences on global trends, as LMEs are the source of 80% of the global marine catch.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it