Predicting the interactions between “ecologically equivalent” indigenous and nonindigenous brachyurans
Bibliographic record
Abstract
Predictive models used to determine the impacts of nonindigenous brachyurans on their “ecological equivalents” in marine ecosystems are sorely lacking. Determining the spatial and temporal extent and magnitude of such impacts by nonindigenous species is difficult because of the broad range of qualitative and quantitative criteria currently used to describe their effects. Forecasting potential impacts requires the development of predictive models that incorporate the effects of interspecific interactions and the mechanisms that give rise to these interactions. Successful validation of such models requires improved techniques for measuring and estimating the functional responses on bioenergetic processes across species compositions, abundances, and environmental conditions. Species-specific information used to support predictive modeling of nonindigenous brachyurans is currently heavily biased towards (i) estimating per capita consumption and growth rates in laboratory conditions and (ii) incorporating the effects of abiotic and biotic factors on these measures. Robust predictive models require repetitive experimentation that advances the understanding of species’ interactions (beyond consumption alone) across variable densities and considers their effects across different spatial and temporal scales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".