Wolves and lynx: Plausible ideas make for testable hypotheses
Bibliographic record
Abstract
Abstract We recently wrote an opinion piece (Ripple et al. 2011) hypothesizing that the presence of wolves ( Canis lupus ) could indirectly benefit Canada lynx ( Lynx canadensis ) by suppressing competition with coyotes ( Canis latrans ). Subsequent comments by Hodges (2012) and Squires et al. (2012) raise several issues regarding our essay. In our reply, we 1) emphasize that many valid hypotheses precede rather than follow data collection, 2) provide additional evidence showing negative effects of wolves on coyotes and an inverse relationship between wolf and coyote abundance, 3) indicate that wolves could increase the availability of hares to lynx by reducing not only hare predation by coyotes but also potential impacts on hare habitat by ungulates, and 4) reject the notion that our expressed views conflict in any way with existing conservation and recovery goals for lynx in the continental United States. We conclude by reaffirming our support for opinion pieces in professional journals, whether or not they are buttressed by large amounts of data, as vehicles for new ideas and catalysts for scientific debate and discussion. © 2012 The Wildlife Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".