Territorial black-capped chickadee males respond faster to high- than to low-frequency songs in experimentally elevated noise conditions
Bibliographic record
Abstract
This is the data for the manuscript: Territorial black-capped chickadee males respond faster to high- than to low-frequency songs in experimentally elevated noise conditions Metadata as follows: Each row corresponds to a single playback trial. Two rows constitutes a paired playback dyad. ID - ID of focal male region - region where playback was performed (van = Vancouver; kel = Kelowna; que = Quesnel; prg = Prince George) hab - Urbanization index (see LaZerte et al. 2017 DOI: 10.1007/s11252-017-0652-7) spl - Sound Pressure level in dB(Z) measured after each trial spl_scale - SPL centered around zero treatment - Contrast category including playback order and stimulus type pb_freq_c - Which stimulus type was presented (Categorical frequency) pb_order - Order of stimuli presentation pb_freq - Dominant frequency of bee-note in stimulus in kHz pb_file - File name of the playback stimulus start_dist - Starting distance in meters of the focal male first_rxn - Response, latency to first response PC1_dist - Response, PC1 Approach and stay close PC2_song - Response, PC2 Sing more time_0_10 - Contribution to PCs, Time spent < 10m from speaker time_10_20 - Contribution to PCs, Time spent between 10 and 20m from speaker time_20_100 - Contribution to PCs, Time spent > 20m from speaker latency_dist_min - Contribution to PCs, Latency (s) to minimum distance from speaker dist_min - Contribution to PCs, Minimum distance from speaker total_song - Contribution to PCs, Total songs sung during trial
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".