Mid-Infrared Spectroscopy Study of Effects of Neonicotinoids on Forager Honey Bee ( <i>Apis mellifera</i> ) Fat Bodies and Their Connection to Colony Collapse Disorder
Bibliographic record
Abstract
Abstract This study investigated the negative effects of neonicotinoid pesticides on honey bees in environment surrounding areas of pesticide use. The aim of the experiment is to identify possible contributors to the sudden decrease in honey bee population over the past 60 years, a phenomenon known as Colony Collapse Disorder. Analysis was performed on three sets of bees: the control group which was not in contact with pesticides, the infected dead group which was a set of bees suspected to have died due to neonicotinoids, and the infected alive group which was suspected to be under the influence of neonicotinoids. After dissecting the bee samples and extracting their fat bodies, the chemical composition and protein structures of the samples were analyzed using Mid-Infrared Beamline at the Canadian Light Source. Results from the spectra of bee samples exposed to neonicotinoids demonstrated possible residual pesticide chemicals within fat bodies. Several spectral peaks were also correlated with a possible change in protein secondary structures from primarily β-sheet to α-helix within fat bodies of neonicotinoid-affected bees. It is likely that the pesticides caused the growth of additional α-helical structures, which is consistent with consequences of the inhibition of nicotinic acetylcholine receptors (nAChRs) a current pathway of harm of Colony Collapse Disorder as identified in past literature.
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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) | 0.001 | 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".