A historical perspective on the effects of trapping and controlling the muskrat (<i>Ondatra zibethicus</i>) in the Netherlands
Bibliographic record
Abstract
BACKGROUND: The muskrat is considered to be a pest species in the Netherlands, and a year-round control programme is in effect. We aimed to evaluate the effectiveness of this programme using historical data on catch and effort collected at a provincial scale. RESULTS: The development of the catch differed between provinces, depending on the year of colonisation by muskrat and the investment of effort (measured as field hours). The catch did not peak in the same year for the various provinces, and provinces that were colonised earlier in time took longer to attain the peak catch. Trapping resulted in declining populations, but only after a certain threshold of annual effort in trapping had been surpassed. On average, populations were observed to decline when the annual effort exceeded 1.4 field hours per km of waterway for several successive years. Having reached a phase of greater control, control organisations tended to reduce effort. CONCLUSION: We conclude that control measures can make muskrat populations decline, provided that the effort is commensurate with the population size. Our study emphasises that experimentation is needed to confirm the causality of the findings, to establish the relation with damage or safety risk and to derive an optimal control strategy. © 2016 Society of Chemical Industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".