Reply to the comment by Harron on “Widespread declines in woodland caribou (<i>Rangifer tarandus caribou</i>) continue in Alberta”
Bibliographic record
Abstract
Estimation of demographic trends from vital rates provides a powerful means to estimate population trends in cryptic or difficult to study species such as woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)). Using such methods, Hervieux et al. (2013; Can. J. Zool. 91(12): 872–882) recently showed 11 of 14 woodland caribou populations in Alberta were declining at ∼8%/year following up to 18 years of monitoring. Harron (2015; Can. J. Zool. 93(2): 149–150) critiques our original study, claiming that negative biases in our demographic monitoring exaggerate our conclusions of widespread caribou declines. Here, we systematically review each of Harron’s claims of bias, rejecting each of his claims upon careful review of the mechanisms by which his purported claims would manifest in our population trend estimation. Therefore, we conclude that Harron’s scientific critique was superficial and misleading. Delays in conservation actions raised by Harron’s critique risk diminishing opportunities to conserve and recover this federally and provincially protected species.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.035 | 0.042 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".