Optimal sampling methods for modelling the occupancy of Arctic grayling (<i>Thymallus arcticus</i>) in the Canadian Barrenlands
Bibliographic record
Abstract
In occupancy models, imperfect detectability of animals is usually corrected for by using temporally repeated surveys to estimate probability of detection. Substituting spatial replicates for temporal replicates could be an advantageous sampling strategy in remote Arctic regions, but may lead to serious violations of model assumptions. Using a case study of site occupancy of adfluvial young-of-year Arctic grayling (Thymallus arcticus) in Barrenland tundra streams, we assessed the reliability and efficiency of alternative sampling strategies: (i) randomly distributed versus sequential adjacent spatial replicates; (ii) visual versus electrofishing surveys; and (iii) spatial versus temporal replicates. Sequential, adjacent spatial replicates produced spatially autocorrelated data. Autocorrelation was relieved using randomly distributed spatial replicates, but using these randomly distributed spatial replicates introduced significant error into estimates of the probability of occupancy in streams. Models designed for spatially autocorrelated data could minimize this bias. Visual and electrofishing surveys produced comparable probabilities of detection. Spatially replicated surveys performed better than temporal replicates. The easiest and relatively most cost-effective sampling methods performed as well as, or better than, the more established, expensive, and logistically difficult alternatives for occupancy estimation.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".