A priori prediction of an extreme crash in 2015 for a population network of the alpine butterfly, <i>Parnassius smintheus</i>
Bibliographic record
Abstract
Prediction provides important validation for scientific hypotheses and models. We update an existing model and present a priori predictions for the growth of a network of 21 populations of the butterfly Parnassius smintheus based on previous population size and climate during the overwintering period. The model predicts that the extremely warm, dry winter of 2015 in the Rocky Mountains will result in a network‐wide crash. All populations are expected to show extreme negative growth. Ten of 21 populations are expected to have less than one individual and the 95% confidence intervals of 17 of 21 populations are predicted to overlap zero in 2015. Given the unprecedented nature of climate change, these predictions represent the best estimate based on our understanding of the effects of climate and density‐dependence for the population growth for this species. If the predictions prove to be valid, it provides strong support for the predictive ability of the current model and the negative impact of extreme climatic events for the persistence of populations and ultimately species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".