Bibliographic record
Abstract
Ecosystem models are important tools for addressing complex issues such as the role of habitat in marine resource management. The Ecopath with Ecosim (EwE) software can represent several ecological processes via mediation functions, where the abundance of one group influences trophic interactions between two other groups. I ran a series of temporal simulations in EwE, in which eelgrass (Zostera marina) was refuge habitat for juvenile Pacific salmon (Oncorhynchus spp.), and the abundance of eelgrass mediated (reduced) the vulnerability of juvenile salmon to their predators. I compared the effects of eelgrass on salmon biomass across three shapes of mediation curve (linear, hyperbolic, sigmoid) and six different initial states along each curve. Salmon responded strongest to sigmoid mediation and least to hyperbolic mediation. Salmon responses were sensitive to initial conditions, particularly along sigmoid curves. As the lower limit of the mediation curve (Mmin) approached 0, model results became nonintuitive, particularly for sigmoid curves. Because these functions are difficult to quantify or scale from empirical or experimental studies, modelers must carefully account for uncertainty when using mediation relationships in EwE. Hyperbolic mediation curves may be the most conservative when empirical or theoretical knowledge is unavailable.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.009 |
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".