Extreme cold weather causes the collapse of a population of <i>Lambdina fiscellaria</i> (Lepidoptera: Geometridae) in the Laurentian Mountains of Québec, Canada
Bibliographic record
Abstract
Abstract In 2012, an unexpected outbreak of Lambdina fiscellaria (Guenée) (Lepidoptera: Geometridae) occurred in the Laurentian Mountains, Québec, Canada, known for its harsh climate. We wondered whether the eggs were sufficiently cold hardy to survive there and, if so, how long this outbreak would last. Therefore, we assessed the capacity of the eggs to supercool, to tolerate short exposures to low sub-zero temperatures, or to successfully overwinter in the field. The same assays were performed with eggs from the island of Newfoundland, Newfoundland and Labrador, Canada. The mean supercooling point of eggs from the two populations increased from −40.2 °C in mid-February to −33.7 °C in mid-May. These eggs may also die at sub-zero temperatures above their supercooling point, depending on exposure durations. In the fall of 2012 when eggs were put out in the field, < 10% survived in the Laurentian Mountains, whereas > 70% survived further south. In the spring of 2013, no parasitism was detected in the population. However, the two cold waves that swept across the Laurentian Mountains the preceding winter were likely responsible for the collapse of the population. This study demonstrates that L. fiscellaria eggs may succumb to sub-zero temperatures above their supercooling point under field conditions.
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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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".