Criticism mismatched: Response to de Keyzer et al. 2016
Bibliographic record
Abstract
In a recent paper, we reported on the evolution of shorter tongues in two alpine bumble bee species in response to climate-induced flower deficits. De Keyzer et al. concede that tongue lengths have decreased but criticize the level of support for our claims. Here, we address the alternative mechanisms they proposed, highlight evidence presented in the supplementary material, and elaborate on the support for our claims in the literature. De Keyzer et al.’s criticisms reflect concerns about the misrepresentation of our work in the popular press. To clarify, we do not imply that evolutionary rescue is necessarily a prudent conservation strategy; we illustrate that remote bumble bee populations buffered from other environmental stressors have undergone an adaptive evolutionary response to dwindling resources under climate change.
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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 | 0.043 | 0.238 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.048 | 0.068 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".