Geographic Distribution of Phosphine Resistance and Frequency of Resistance Genes in Two Species of Grain Beetles, Tribolium castaneum and Rhyzopertha dominica, in North America
Bibliographic record
Abstract
Resistance to the fumigant phosphine (PH3) was studied for 28 populations of Rhyzopertha dominica from eight states of the USA and four provinces of Canada, as well as for 34 populations of Tribolium castaneum from twelve states of the USA and four provinces of Canada, using both a discriminating dose bioassay and molecular marker analysis. We used a molecular marker analysis for a point mutation in the gene that encodes dihydrolipoamide dehydrogenase and facilitates the “strong resistance” phenotype in both species. Our results showed that PH3 resistance was correlated with higher frequencies of the strong resistance R allele in both species (R2 = 0.59 in R. dominica and R2 = 0.79 in T. castaneum). We also found that recessive R allele frequency did not correlate well with the geographic distribution of the resistant populations of these two species (R2 = 0.21 in R. dominica and R2 = 0.15 in T. castaneum). Therefore, populations of both species with higher R allele frequencies had higher resistance levels to PH3. Our results showed that the geographic distribution of PH3 resistance in both species varied and was not related geographically, but this supports the idea that the adaptive evolution of PH3 resistance in these species is caused by selection pressure for their resistance genes.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".