Estimating EAF indicators from scientific trawl survey : theorretical and pratical concerns
Bibliographic record
Abstract
Under the context of an ecosystem approach to fisheries (EAF), there is keen interest in providing insights into the evolution of exploited ecosystems using simple ecosystem indicators.Many nations have long-term scientific research surveys, originally driven by conventional approaches in fisheries assessment and management.The aim of this study is to address the practical concerns linked to current objectives of monitoring simple EAF indicators, using data from surveys that were not historically designed for the purpose.Based on the results of an expert survey designed to collect expert knowledge on research surveys from scientists working on different ecosystems worldwide, a list of challenges faced during indicator estimation is highlighted, along with associated concerns and constraints.The work provides additional information useful in the interpretation of the results obtained on the state and trends of ecosystems using EAF indicators by the IndiSeas WG.Further, the related discussion provides potential pathways that could be useful for future research and development aiming to improve the ecosystem indicator approach in the operational context of EAF.The question of the utility for EAF of using historical dataseries of scientific trawl series is also discussed.Such long-term series are concluded to be useful, that they are even inescapable (since the past cannot be resamplied), and that EAF therefore brings a supplementary reason for continuing such monitoring and to incorporate new insights in how research surveys may be conducted.
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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 | 0.028 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".