Estimating the carrying capacity of French Frigate Shoals for the endangered Hawaiian monk seal using Ecopath with Ecosim
Bibliographic record
Abstract
Abstract The carrying capacity of the French Frigate Shoals (FFS) region for the endangered Hawaiian monk seal was appraised using an updated version of the original FFS Ecopath model ( Polovina 1984 ). Model parameters were updated using recent literature, and data from surveys of the seal population and its bottom‐associated prey. Together they produced a static mass balance model for 1998 when the prey surveys began. The Ecopath‐estimated monk seal biomass was 0.0045 t/km2, which was in close agreement with the biomass calculated from monk seal field beach counts (0.0046 t/km2). Model simulations through time were done in Ecosim using the Ecopath balanced model and included fisheries data time series from 1998 to 2008. Monk seal biomass declined concurrently with decreases in benthic bottomfish biomass, which were influenced by large‐scale changes in the environment of the North Pacific. This model scenario was extended from 2010, when the last permitted fishery in the Northwestern Hawaiian Islands was closed, through to 2040, assuming a constant environmental signal. Model results for this time period did not show a recovery of monk seals that exceeded the initial 1998 model biomass levels, highlighting the importance of including environmental variability in estimates of monk seals recovery at FFS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".