Noise from four types of extractive energy infrastructure affects song features of Savannah Sparrows
Bibliographic record
Abstract
Human activities change the acoustic environment in many settings around the world.These changes are complex, as different anthropogenic sound sources create different acoustic profiles; therefore, some sound sources may have greater impacts on wildlife than others.Animals may adapt to these altered acoustic environments by adjusting their vocalizations.In the case of bird song, this may be achieved by adjusting the whole song, which may preserve the spectral and temporal relationships between successive syllables, or by adjusting components (syllables) within the song.Determining which syllables are adjusted may help to elucidate the mechanisms, benefits, and limitations of song plasticity.We examined the effects of conventional industrial infrastructure used to extract shallow natural gas and petroleum (natural gas compressor stations, generator-powered oil well pumpjacks, power grid-powered oil well screw pumps, and generator-powered oil well screw pumps), compared with quiet control sites, on 15 variables describing the acoustic properties of whole songs and syllables of Savannah Sparrows (Passerculus sandwichensis).Changes in acoustic properties varied with syllable and infrastructure type; most effects occurred at generatorpowered screw pump sites, the loudest type of infrastructure that we studied.We found no effects of infrastructure on whole song variables, but song minimum frequency increased with ambient background noise.We found differing effects of infrastructure type on individual syllable types, with alterations in frequencies, tonalities, and syllable durations.This suggests that some syllables may be more plastic in their response than others, which may allow Savannah Sparrows to adapt their communication in altered acoustic environments.
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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 | 0.000 |
| 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) | 0.001 | 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".