A TECHNIQUE THAT INCREASES DETECTABILITY OF PASSERINE SPECIES DURING POINT COUNTS
Bibliographic record
Abstract
During April–July 1997, we censused birds in three woodlands near Arnprior, Ontario, Canada using conventional point counts (n = 12) and point counts supplemented with “pishing” (n = 12), a well-known method for attracting various bird species. Overall, 3.6 (19%) more species were detected per census using pishing. Irrespective of statistical significance of individual species, 45 (74%) of the 61 species were detected on more days using pishing, whereas 5 (8%) species were detected on more days using the conventional method. A higher number of males and a higher number of visually detected species were recorded using pishing as compared to the conventional method, and these differences did not change with date. Pishing did not affect number of females detected nor number of species aurally detected. More individuals were detected using pishing as opposed to the conventional method, but the difference declined with date. Overall, 0.8 fewer unidentified individuals per census were recorded using pishing. Our results indicate that pishing in conjunction with conventional point count methods increases detectability and positive visual identification of passerine species in woodlands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.028 | 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 teacher head, 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".