Long-term automated monitoring of the distribution of small carnivores
Bibliographic record
Abstract
A new automated monitoring device for small carnivores, the Scentinel®, is a ‘smart’ tracking tunnel. It records time, date, weight and a digital photograph of every animal visiting it, and stores the data to be downloaded on command. This paper describes a field trial aiming, first, to verify the Scentinel’s species identifications against those given by footprint tracking papers, and then to compare the efficacy of routine monitoring with the Scentinel against standard tunnel tracking methods. In February–April 2005 we identified to species 98% of 1559 visiting animals, mainly hedgehogs (Erinaceus europaeus), ferrets (Mustela furo), cats (Felis catus) and rats (Rattus rattus and R. norvegicus) in 1718 Scentinel-nights. In May–June 2005 we set up three monitoring lines 1 km apart, each with 10 tracking tunnels and two Scentinels. We recorded 656 visits by ship rats (Rattus rattus), 88% of them on only one of the three lines, in 198 Scentinel-nights (over 5 weeks). The 30 footprint tracking tunnels set intermittently (360 trap-nights) recorded high (70–100%) tracking rates on all lines. The presence of a stoat (Mustela erminea) was detected by both methods, but earlier by Scentinels than by tracking tunnels. These results confirm that it is possible to use automated devices to record detailed monitoring data on small carnivores in remote areas over long periods, unaffected by interference or bait loss from common 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".