Detecting a population decline of woodland caribou ( <i>Rangifer tarandus caribou</i> ) from non‐standardized monitoring data in Pukaskwa National Park, Ontario
Bibliographic record
Abstract
ABSTRACT Observation bias from methodological inconsistencies plague many long‐term ecological monitoring studies, leaving land managers to question the validity of apparent population trends over time. Furthermore, some species are cryptic and have low detectability, so assessments are naturally imprecise. We assessed the utility of aerial surveys for woodland caribou from 1972 to 2009 at a Canadian national park in detecting a reliable population trend for this threatened species. The surveys varied in flight patterns, total distance flown, observer experience, speed, altitude, timing, temperature, and snow depth. Of these, distance and the speed/altitude index influenced the population estimates, whereas no variables influenced the calf:female ratio or winter range size. Year was included in all plausible models for population estimates, and the majority of plausible models for calf:female ratio and winter range size. Population size, recruitment and winter range size all declined over time in the respective models with the lowest AIC c . Switching methodologies mid‐way through a long‐term aerial survey monitoring program creates greater complexity for trend analysis over time; however, this study suggests that reliable conclusions can still be drawn from long‐term monitoring data if confounding factors are accounted for in the analysis. © 2014 The Wildlife Society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".