Data from: Host specificity in subarctic aphids
Bibliographic record
Abstract
The specificity of parasitic interaction depends on the adaptations of both the host and the parasite. Over time, these interactions evolve and change as a result of an “arms race” between host and parasite, and the resulting species-specific adaptations may be maintained, perpetuating these interactions across speciation events. With speciation and species sorting over time, complex systems of interactions evolve. Here, we elucidate some of these interactions using the aphids (Hemiptera: Aphididae) of Churchill as a model system. We analyzed these interactions by testing for two patterns in host-specificity: monophagy and phylogenetic clustering. We defined monophagy as one species feeding upon a single host plant species, an association which is driven by arms races in morphology, chemical resistance/tolerance, and camouflage; this pattern was observed in 7 of 22 aphid species. Secondly, we observed three separate cases where groups of closely related aphid species fed upon individual plant species (examples of phylogenetic clustering). One explanation for uncovering species-specific interactions in a recently deglaciated, sub-arctic locality is that the species involved in the associations moved north together. Testing different levels of specificity in species interactions allows us to accurately elucidate these patterns and gives us insight into where to direct future research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.028 |
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".