CALL BROADCASTING AND AUTOMATED RECORDERS AS TOOLS FOR ANURAN SURVEYS IN A SUBARCTIC TUNDRA LANDSCAPE
Bibliographic record
Abstract
Relatively little is known about population ecology of anurans in arctic and subarctic tundra regions, in part because it is difficult to survey anurans in these landscapes. Anuran survey protocols developed for temperate regions have limited applicability in arctic and subarctic tundra landscapes, which may lack roads and vehicle access, and experience variable and inclement weather during short anuran breeding seasons. To evaluate approaches to address some of the limitations of surveying anurans in tundra landscapes, we assessed the effectiveness of using breeding call broadcasts to increase detection of Boreal Chorus Frogs (Pseudacris maculata) and Wood Frogs (Lithobates sylvaticus) near Cape Churchill, Manitoba, Canada. We also evaluated how counts of anurans derived from automated audio recorders compared with those obtained simultaneously by observers. We detected on average 0.4 additional Wood Frogs per survey when we broadcasted calls (x = 0.82, SD = 1.38), an increase of > 40% compared to surveys without broadcasts (x = 1.24, SD = 1.51; Wilcoxon test; Z = 2.73, P = 0.006). In contrast, broadcasting Boreal Chorus Frog calls did not increase the number of chorus frog detections (Wilcoxon test; Z < 0.001, P > 0.90). Detections of Wood Frogs in a 100-m radius were lower via automated recorders (x = 0.60, SD = 0.87 SD) than by observers during simultaneous surveys (x = 0.96, SD = 1.27 Z = 2.07, P = 0.038), but those of Boreal Chorus Frogs were not different (x = 1.72, SD = 1.31;x = 1.44, SD = 1.5; Z = 1.55, P > 0.121). Our results suggest that broadcasting calls can increase detection of Wood Frogs, and that automated recorders are useful in detecting both Wood Frogs and Boreal Chorus Fogs in arctic and subarctic tundra landscapes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".