Escaping the hot seat: consuming decomposing <i>Cannabis sativa</i> slows weight gain and heat escape behaviour in the earthworm, <i>Eisenia fetida</i>
Bibliographic record
Abstract
Given the rise in cannabis ( Cannabis sativa L .) cultivation, and the absence of disposal guidelines in Canada and other countries, the toxicological impacts of its secondary metabolites on non-target organisms via ingestion are important to understand. Due to the unique chemicals found in C. sativa, sublethal effects, such as those on behaviours and growth, must also be considered. We fed a generalist detritivore, the red wiggler earthworm ( Eisenia fetida (Savigny, 1826)), diets composed of hemp or two other crop plants (lettuce or basil), or combinations thereof, over 28 days to measure effects on earthworm survival, growth, and behavioural responses to stressful stimuli (high heat and light). Earthworms fed only hemp gained weight, albeit less than those on other diets, with no effects on their survival or response to light. However, hemp consumption led to slower escape times and speeds from a heat stimulus, indicating potential sublethal effects. By using hemp genotypes with low cannabinoid concentrations, our results offer a conservative estimate of the potential impact of hemp cultivation or disposal of C. sativa on invertebrates such as earthworms. This research underscores the importance of developing accurate risk models for exposure to crop phytochemicals in non-target species.
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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".