Predicting Raccoon, <em>Procyon lotor</em>, Occurrence Through the Use of Microhabitat Variables
Bibliographic record
Abstract
Recent increases in Raccoon (Procyon lotor) abundance have been implicated for decreased nesting success of songbirds and transmission of rabies. Understanding the relationship between occurrence and microhabitat factors should be helpful in managing this species, though our current understanding of this relationship is inadequate. Therefore, we conducted a study in western Tennessee during 2000–2002 to determine this association. Occurrence (capture) data were assessed from results of live trapping at 176 and 112 trap sites during winter and summer, respectively, at three sites. A maximum of 26 habitat variables were measured at each trap location; all grids were combined for statistical analyses to account for varying relationships between occurrence and microhabitat factors across different landscapes. Univariate and stepwise logistic-regression analyses were used to assess associations among microhabitat variables and occurrence. Resulting models were validated through the jackknife procedure. Predictive equations were constructed from logistic-regression models to compute capture probabilities. Univariate analyses yielded numerous significant variables with those representing forest characteristics and proximity to water generally the most significant. Strong concordance was observed between winter and summer seasons for most variables though several differed (number of large hardwood snags, ground dens, and plant food species, distance to potential water and roads). Such temporal variability was expected due to seasonal differences in habitat components and biological needs of Raccoons. Variables included in derived models were similar to those scoring highest in univariate analyses; classification rates for models (winter = 72%; summer = 78%) were among the highest recorded for generalist species. By accounting for landscape attributes and replicating across sites, more accurate and useful models were developed. Such models should provide the information required to effectively manage this 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 | 0.001 |
| 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".