Building an expert judgment-based model of mangrove fisheries
Bibliographic record
Abstract
Mangroves are critically important habitats for fisheries, both\nfor their resident fish, crustacean, and mollusk populations and as nursery\ngrounds for the target species of offshore fisheries. However, the spatial variation\nin the benefits provided by mangroves to fisheries is poorly understood.\nBased on expert knowledge of mangrove ecology and fisheries biology, we developed\na preliminary model of the spatial distribution of benefits to fisheries\nfrom mangroves. The preliminary model covers the environmental factors that\ndetermine the amount of fish, crustaceans, mollusks, and other fishery target\nspecies produced by mangrove areas (termed “potential fish production”) and\nthe socioeconomic variables that determine the level of fishing in any given location.\nThe combination of these two outputs gives the predicted catch. Potential\nfish production is predicted to be highest where there is high freshwater and\nnutrient input to mangroves, such as in large estuaries. At large seascape scales,\ntotal mangrove area is also an important driver. Fishing effort is highest close\nto human populations, which provide both the fishers and the markets for their\ncatch. The model is qualitative and has not been parameterized with field data\nand, as such, should only be considered as a first step towards understanding\nthe spatial variation in the benefits that mangroves provide to fisheries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".