Detectability of Non-passerines Using "Pishing" in Eastern Ontario Woodlands
Bibliographic record
Abstract
During spring and summer 1997, non-passerines were surveyed in three woodlots near Arnprior, Ontario, using standard point counts, and standard point counts combined with “pishing” (pishing involves the observer saying the words “pish pish pish pish” in a continuous series of short bursts). Of the 27 non-passerine species detected, 22 were recorded on more days using pishing as opposed to the standard point count method. However, only three of these species were recorded on significantly more days using pishing. Several woodpecker species approached more closely during point counts with pishing, which facilitated identification. In contrast, raptors and some other non-passerines that may have otherwise gone unnoticed were identified as they fled from the pishing sound. Hence, when the overall goal of research is to detect species richness or to gather presence and absence data in woodlands, point counts combined with pishing may increase detectability of some non-passerines.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".