Rodenticide efficacy of sodium selenite baits in laboratory conditions
Bibliographic record
Abstract
We examined the acceptance and palatability of baits containing different contents of sodium selenite as a rodenticide, in Swiss mice under laboratory conditions. In a no-choice and choice feeding test, the animals were exposed to baits containing 0.1, 0.05, 0.025 and 0.0125% of sodium selenite. The total bait consumption by Swiss mice in the no-choice feeding test was highly negatively correlated, while total sodium selenite intake was medium-positively correlated to the sodium selenite content in the bait. In the same test, daily intakes significantly depended on the content of sodium selenite in the bait, while the exposure and associated interactions of contents of sodium selenite and exposure had no statistically significant impact. Baits with sodium selenite contents of 0.05 and 0.1% had the most lethal effects. The negative impact of the sodium selenite content on bait acceptance and palatability was confirmed in choice feeding tests. Baits containing 0.05 and 0.1% of sodium selenite displayed the biological potential to be used as a rodenticide. It is necessary to improve its insufficient acceptability and palatability by adding adequate additives to the bait. The results of this study should be verified in experiments with wild rodents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".