The future of fisheries science in management: a remote-sensing perspective
Bibliographic record
Abstract
Abstract Stuart, V., Platt, T., and Sathyendranath, S. 2011. The future of fisheries science in management: a remote-sensing perspective. – ICES Journal of Marine Science, 68: 644–650. Earth observation from satellites offers vast potential for fisheries applications, including management of marine resources, stock assessment, marine aquaculture, and fish harvesting. One of the most promising avenues for the use of satellite data for fisheries science in management lies in quantifying objectively the variables that result in large and small year classes of exploited stocks. The influence of fluctuations in the availability of food in the critical period of larval stages can be investigated through the application of ecological indicators describing the variability of the pelagic ecosystem at a given time and place. These indices can increase our understanding of the relationship between ecosystem factors and the recruitment of key species. Despite the many demonstration applications published to date, little use is being made of satellite data to support fisheries science in management. We discuss some of the obstacles that lie in the way of the operational use of satellite data and suggest actions that could facilitate its broader application.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".