Zoo soundscape: Daily variation of low‐to‐high‐frequency sounds
Bibliographic record
Abstract
Abstract Most studies assessing the impact of noises on zoo animal welfare did not measure sound frequencies outside of the human‐hearing range (infrasounds and ultrasounds). Many nonhuman mammals can hear these frequencies, and because loud and variable soundscapes are potentially detrimental for animal welfare, this overlooked aspect of their acoustic environment could have important consequences. This study evaluated the soundscape of an urban zoo in a large frequency range (17.5–90,510 Hz) by measuring its average sound levels (Leq) and variability (the difference between highest and lowest peaks). Sound data were collected for 24 hr in 25 locations (e.g., indoor, outdoor, near the amusement park). The soundscape was not considered problematic for animal welfare when looking at the average sound levels in most locations (<77‐dB sound pressure level [SPL]), except for a few indoor areas and near the water park. Ultrasounds were rare, had low average sound levels, and were less variable in time. Infrasounds were always present and were the loudest and most variable sound frequencies. The soundscape was louder and more variable during the day and when visitors were present, suggesting that human‐related activities were the sources of these augmentations. Indoor environments were generally louder than outdoor environments and touristic features; however, the water park was near 85‐dB SPL during the day. On the basis of results, we suggest a series of mitigation actions to minimize noise‐related stress in captive animals.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".