Comparison of spatial distribution models to predict subtidal burying habitat of the forage fish <scp><i>Ammodytes personatus</i></scp> in the Strait of Georgia, British Columbia, Canada
Bibliographic record
Abstract
Abstract The Pacific sand lance (Ammodytes personatus) is a key forage species for many commercially important fish (e.g. salmon and groundfish), marine birds, and whales found in nearshore coastal waters of British Columbia, Canada. Sand lance lack a swim bladder and have a requirement for low‐silt, medium‐coarse sandy sea‐bed habitat for burying. Little information is available describing the distribution of burying habitat, partly because there are no commercial fisheries for A. personatus in British Columbia. This information is required by habitat and wildlife managers to identify and protect uncommon patches of burying habitats from detrimental activities, including dredging, infilling, and oil spills. In this study, habitat distribution results from five suitability modelling algorithms were evaluated: maximum entropy, generalized linear model, generalized additive model, random forest, and an ensemble model of the latter three. The maximum entropy model had the highest performance score (area under the receiver operator characteristic curve was 0.78) and was selected as the model that most accurately identified the presence of suitable A. personatus burying habitat. Model results indicate that suitable burying habitat is primarily influenced by derived sea‐bed substrate, distance to estuary, distance to sand‐gravel beaches, and bottom sea temperature. Overall, the spatial modelling identified only 105 km2 of highly suitable sand lance burying habitat, or 2.6% of the study area (0–150 m), primarily in Haro Strait, along the east coast of Vancouver Island, and in northern regions of the strait near Cortes, Savary, and Harwood islands. Identification of this uncommon and patchy burying habitat will contribute to the ongoing conservation of an important coastal prey species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".