Data‐free speculation does not make for testable hypotheses: A reply to Ripple et al.
Bibliographic record
Abstract
Abstract The role of top predators in structuring ecosystems is receiving substantial attention from ecologists. Ripple et al. (2011) recently posed a tentatively supported hypothesis that wolves ( Canis lupus ) may help restore populations of the U.S. federally Threatened Canada lynx ( Lynx canadensis ), a specialist predator on snowshoe hares ( Lepus americanus ), through 2 possible mechanisms: a) decreases in the numbers of coyotes ( C. latrans ), which may compete with lynx for hares as prey and may also kill lynx; or b) decreases in ungulates that might compete with hares for food. These speculative opinions are not supported by current data, either across the range or in the extended example of Yellowstone National Park (YNP) that Ripple et al. provide. The coyote hypothesis lacks the required quantification of coyote numbers and predation rates on hares, both in YNP and across the range of lynx. The browse competition hypothesis ignores substantial differences in browse and habitat preferences between elk ( Cervus elaphus ) or livestock and hares that prevent competition for food. Our previous work in YNP (Hodges et al. 2009) showed scattered and very low densities of snowshoe hares, with distributions driven by the substantial fire‐related habitat changes YNP has experienced over the past century. Although I applaud their interest in lynx conservation, the unsupported speculations of Ripple et al. do not advance our ability to manage lynx or hares, nor do they present plausible directions for conservation research. © 2012 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 | 0.049 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.007 | 0.023 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.056 | 0.118 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".