Optimal weighting in species habitat modeling: a case study from <i>Ommastrephes bartramii</i> in the Northwest Pacific Ocean
Bibliographic record
Abstract
The weighting of environmental variables in habitat modelling is important, especially for species with a poorly understood distribution. Traditional weighting schemes, such as arithmetic or geometric mean, often cause “gradient” habitat distribution patterns. We develop a new methodology that determines optimal variable weighting via a structured sensitivity analysis approach. This method considers the full spectrum of weighting combinations and uses multiple model selection criteria to select the best fit. We use a Northwest Pacific neon flying squid (Ommastrephes bartramii) fishery dataset (1998–2012) to compare our best performance habitat suitability index (BEST-HIS) with the traditional fixed methods, as well as to the more recent machine learning approach: boosted regression tree. Approaches were evaluated based on differences in habitat metrics, such as continuity, magnitude, and ratio of estimated unfavourable/favourable habitat. The BEST-HSI model generally outperformed the other three methods, though habitat metrics notably differed depending on weighting schemes used. The BEST-HSI approach is an efficient exploratory tool to investigate empirical relationships between organism presence and the environment, particularly for species with little known life history or migration information.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".