Use of Carabids for the Post-Market Environmental Monitoring of Genetically Modified Crops
Bibliographic record
Abstract
Post-market environmental monitoring (PMEM) of genetically modified (GM) crops is required by EU legislation and has been a subject of debate for many years; however, no consensus on the methodology to be used has been reached. We explored the suitability of carabid beetles as surrogates for the detection of unintended effects of GM crops in general PMEM surveillance. Our study combines data on carabid communities from five maize field trials in Central Europe. Altogether, 86 species and 58,304 individuals were collected. Modeling based on the gradual elimination of the least abundant species, or of the fewest categories of functional traits, showed that a trait-based analysis of the most common species may be suitable for PMEM. Species represented by fewer than 230 individuals (all localities combined) should be excluded and species with an abundance higher than 600 should be preserved for statistical analyses. Sixteen species, representing 15 categories of functional traits fulfill these criteria, are typical dominant inhabitants of agroecocoenoses in Central Europe, are easy to determine, and their functional classification is well known. The effect of sampling year is negligible when at least four samples are collected during maize development beginning from 1 April. The recommended methodology fulfills PMEM requirements, including applicability to large-scale use. However, suggested thresholds of carabid comparability should be verified before definitive conclusions are drawn.
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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 | 0.001 |
| 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 | 0.001 | 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".