Building an expert judgment-based model of mangrove fisheries
Bibliographic record
Abstract
Mangroves are critically important habitats for fisheries, bothfor their resident fish, crustacean, and mollusk populations and as nurserygrounds for the target species of offshore fisheries. However, the spatial variationin the benefits provided by mangroves to fisheries is poorly understood.Based on expert knowledge of mangrove ecology and fisheries biology, we developeda preliminary model of the spatial distribution of benefits to fisheriesfrom mangroves. The preliminary model covers the environmental factors thatdetermine the amount of fish, crustaceans, mollusks, and other fishery targetspecies produced by mangrove areas (termed “potential fish production”) andthe socioeconomic variables that determine the level of fishing in any given location.The combination of these two outputs gives the predicted catch. Potentialfish production is predicted to be highest where there is high freshwater andnutrient input to mangroves, such as in large estuaries. At large seascape scales,total mangrove area is also an important driver. Fishing effort is highest closeto human populations, which provide both the fishers and the markets for theircatch. The model is qualitative and has not been parameterized with field dataand, as such, should only be considered as a first step towards understandingthe spatial variation in the benefits that mangroves provide to fisheries.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".