Ranking the ecological relative status of exploited marine ecosystems
Bibliographic record
Abstract
Abstract Coll, M., Shannon, L. J., Yemane, D., Link, J. S., Ojaveer, H., Neira, S., Jouffre, D., Labrosse, P., Heymans, J. J., Fulton, E. A., and Shin, Y-J. 2010. Ranking the ecological relative status of exploited marine ecosystems. – ICES Journal of Marine Science, 67: 769–786. A set of simple, data-based ecological indicators was used to rank exploited ecosystems regarding fishing impacts with respect to their status, trends, and ecosystem EAF attributes. Expected theoretical changes in indicators with respect to increasing fishing impacts were considered, and ecosystems were compared by examining the mean values of indicators in the most recent three years for which data were available and over time (1980–2005 and 1996–2005). Systems were classified into nine potential categories according to whether they were most, moderately, or least impacted, and whether they were becoming more or less impacted, or remaining stationary. The responses of ecological indicators to additional environmental and socio-economic explanatory factors were tested. Ecosystems ranked using short- and long-term trends and states differed because of differences in trends, underscoring the importance of analysing both states and trends in ecosystem analyses. The number of ecosystems classified as unclear or intermediately impacted has increased recently, the proportion of ecosystems classified as less strongly impacted has been maintained, but more now fall within the category more strongly impacted in terms of long-term trends and states. Ecosystem type, fisheries enforcement, primary production, sea temperature, and fishing type were important variables explaining the ecological indicators. The results reflect different changes and processes in the ecosystems, demonstrating that information on ecological, environmental, and fishery histories is crucial to interpreting indicators correctly, while disentangling the effects of fishing and of the environment.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".