Data from "Burrowing depth mediates the extent of metabolic suppression in a soil-overwintering insect"
Bibliographic record
Abstract
Metadata are provided in each data sheet. Links to a Shiny app of an interactive simulation, and the underlying R script (on GitHub) are provided below. Refer to manuscript for full details. Abstract 1. Conserving energy through winter is important for the fitness of temperate insects. While insects can use buffered microhabitats, metabolic suppression, or decreases in the thermal sensitivity of metabolic rate to override seasonal-scale thermal trends, the relative importance of these strategies for limiting energy use by insects overwintering in soil remains largely underexplored. 2. We used a combined laboratory, field, and simulation approach to investigate the overwintering energetics of the western bean cutworm (Striacosta albicosta), a univoltine lepidopteran pest of dry beans and corn that overwinters underground as a dormant prepupa. 3. We hypothesized that (1) the selection of thermally-buffered microhabitats (i.e. deeper soil sites) reduces energy use in early autumn and late spring, and that (2) changes in the metabolic rate-temperature relationship reduce the impact of elevated temperatures on overwintering energy use. 4. We provide evidence that during the warmest parts of winter, dormant S. albicosta prepupae that had burrowed deep benefited from a cool, stable microclimate, whereas those near the soil surface appeared to rely on deeper metabolic suppression to maintain their energy stores. 5. Although elevated temperatures in the laboratory depleted their energy reserves, these strategies appear sufficient to limit energy drain under natural conditions in the field. 6. We suggest that small-scale variation in the depth of soil refuges may mediate the interaction between the risk of energy drain and changes in the metabolic rate-temperature relationship in soil-overwintering insects.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.134 | 0.023 |
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".