Environmental Features Associated With At‐Sea Sightings of Snow Petrel <i>Pagodroma nivea</i> in East Antarctica
Bibliographic record
Abstract
) and its marine habitat use-especially in East Antarctica. To better understand what drives Snow Petrel presence within this region, we modeled vessel-based observations of the Snow Petrel against remotely sensed environmental data using binomial generalized additive models (GAMs). Throughout the 16-year study period (1991-2006), Snow Petrel presence was associated with areas exhibiting shallower bathymetry, increasing sea-ice coverage, decreasing sea-surface height, and increasing wind speed. We then used a subset of the Snow Petrel data to generate a population density map and compare model outputs when data recording methods differ. Specifically, we tested how outputs change when inputs are binomial (presence/absence) versus when inputs include count and effort data. The outputs from both effort-quantified and presence/absence models identified similar environmental drivers of Snow Petrel presence. However, the effort-quantified models were more robust, yielding higher deviance explained values and narrower confidence intervals around the environmental variables associated with Snow Petrel presence. Snow Petrel interactions with the tested environmental variables may be driven by associated biological processes-specifically, foraging strategies that target niche areas of high biological productivity in the Southern Ocean. Our study provides an important baseline to compare the likely future distribution of Snow Petrels under different climate change scenarios.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".