Modelling the potential impact of climate change on future spatial and temporal patterns of biological control agents:<i>Peristenus digoneutis</i>(Hymenoptera: Braconidae) as a case study
Bibliographic record
Abstract
Abstract Mechanistic species niche models were used to map the seasonal spatio-temporal dynamics of biological control pressure. Future climate scenarios were applied to these models to identify potential future trends in the patterns of biological control pressure through space and time during an annual seasonal cycle.Peristenus digoneutisLoan (Hymenoptera: Braconidae) is a parasitoid ofLygusHahn (Hemiptera: Miridae) species, important pests of glasshouse and field crops throughout Europe and North America. Consistent with theoretical expectations, the modelled potential range ofP. digoneutisexpanded polewards and contracted from its southern temperature range limits. However, its distribution did not change consistently across continents or countries. Locations near the outer limits of the current modelled distribution were more sensitive to changes in future climates than locations near the central core. Weekly climate suitability and stress maps were developed to provide insight into seasonal adjustments that accompany changes in the potential range of pest species and their natural enemies. Climate change may increase the number ofLygusgenerations in western Canada allowingP. digoneutisto establish in areas, where biological control attempts had failed in the past.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".