PREDICTING SUITABLE MARINE HABITAT FOR PINK-FOOTED SHEARWATERS (<i>ARDENNA CREATOPUS</i>) IN THE WATERS ALONG THE PACIFIC COAST OF CANADA
Bibliographic record
Abstract
This set of files contains the R code and input data used to run a Generalized Additive Model (GAM) for Pink-footed Shearwaters (PFSH) in Canadian Pacific waters. The model uses standardized at-sea survey data (1992–2019) to predict species presence and relative abundance in relation to key environmental covariates.The .R script includes data preparation, model fitting, validation, and prediction steps. The accompanying .csv file provides the standardized 2 km² observation dataset, and the shapefile defines the study area boundary used for mapping model outputs. Manuscript Abstract: Anthropogenic activities are threatening global marine ecosystems, with seabirds representing a vulnerable group that has experienced pronounced population declines in recent decades. The ability to identify important marine areas for vulnerable seabirds is fundamental to conservation initiatives. The Pink-footed Shearwater (Ardenna creatopus; listed as Endangered in Canada) breeds only in Chile, but during the non-breeding season it ranges northward to waters off Canada’s Pacific coast and the northern Gulf of Alaska. Utilizing at-sea survey data spanning from 1992 to 2019, we examined the relationship between the species’ distribution and environmental variables using a two-step Generalized Additive Model approach. Cross-validation with out-of-sample testing showed high predictive accuracy for occurrence (AUC = 0.94) and moderate performance for abundance predictions (Spearman’s rank correlation = 0.32, RMSE = 3.92, MAE = 0.45) at a 4 km² resolution. The results give us confidence in the model’s ability to identify areas suitable for Pink-footed Shearwaters. Distribution was strongly associated with several oceanographic and geographic factors, particularly latitude and distance to the continental shelfbreak. The findings of this study may help inform marine conservation efforts within Canada’s Pacific Exclusive Economic Zone and beyond.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".