Feasibility of the application of electronic nose technology to monitoring insect infestation in wheat
Bibliographic record
Abstract
An Alpha MOS FOX-3000 electronic nose equipped with 12 Metal Oxide Semiconductor (MOS) sensors was used to evaluate the presence of two insects in wheat.Canada Western Red Spring (CWRS) wheat (cv.AC Barrie) infested with rusty grain beetle, Cryptolestes ferrugineus (Stephens), or red flour beetle, Tribolium castaneum (Herbst), were placed in the glassjars (4 L capacity).Different numbers ofjnsects (0, 1,2, l0 and 20 insects/kg) \¡r'ere tested for each insect species in combination with three moisture content levels for the grain (l4o/o,160/o, and 18%).The headspace volatiles from infested or non-infested wheat was sampled and injected into the sensor array.Each individual sample collected was analyzed in triplicate and each treatment was tested seven times.The response ofgas sensors, in the form ofa multi-dimensional matrix, was However, the electronic nose did not detect the presence of rusty grain beetle (RGB) in wheat at either the low (l insect/kg and 2 insects/kg) or the high infestation levels (10 insectslkg and 20 insects/kg).It also failed to detect the presence of RFB in wheat with the low (l inseclkg and 2 insects/kg) and high infestation level (20 insects/kg) at 18% moisture content.The electronic nose could differentiate I RFB/kg infestation level from 20 RFBs/kg infestation level in wheat at l4%;o and l60% moisture content.Hor¡r'ever, it did not identifu densities for rusty grain beetle in wheat.High percentage of recognition (99%) and high cross-validation (0.99) were achieved for predicting moisture content of noninfested wheat.lt¡ 56 63 vlll
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".