A rapid method to detect <i>Cryptolestes ferrugineus</i> (Coleoptera: Cucujidae) larvae in stored grain
Bibliographic record
Abstract
Modifications of three detection methods, based on the heat-gradient principle employed by the Berlese funnel method were investigated to extract larvae of the rusty grain beetle, Cryptolestes ferrugineus (Stephens), developing under the seed coats of infested kernels in grain samples. One-kilogram samples of wheat, barley and corn were artificially infested with rusty grain beetle eggs, resulting in survival rates (to larval stage) of 71.1 ± 14.4, 58.9 ± 14.3, and 24.7 ± 11.8%, respectively. Sets of 10 infested kernels containing different-aged larvae (10 individuals × 4 instars) were added to 1-kg samples of hydrated grain, then heated on screens beneath heat sources (lights) at several heights in three different containers (Berlese funnel, with a 7-cm-deep grain layer, or square and rectangular screened boxes with a grain layer several kernels deep). There were no significantly different extraction rates between the rectangular and square containers for all heating trials. A larval extraction rate of 31% was produced by 1-h trials with wheat (15% moisture content wet basis) 5.5 cm below the lamp bank with a thermostat set at 50°C. This matched the efficiency of the Berlese funnel method (36% extraction in 6 h), but in much less time. Similar results were found for barley, bu t for corn the square and rectangle gave significantly better extraction than the Berlese funnel, although extraction efficiency was half that of wheat and barley. Considerably lower extraction rates were obtained from trials that did not use a thermostat. The results from this experiment show that there is a potential alternative detection method with the thermostatically controlled heating of a thin layer of grain that should be faster than the conventional 6-h Berlese funnel method. Key words: Cryptolestes ferrugineus, grain, larval extraction, Berlese funnel, grain monolayer
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".