Bibliographic record
Abstract
Sounds are one of the most common means of communication and are essential to several species ’ survival and reproduction. The efficiency of acoustic signal transmission, and the ability of receivers to detect that signal, can be affected by ambient noise, such as that produced by human activities. Recent studies have suggested that animals alter the frequencies of their acoustic signals to minimize interference produced by anthropogenic noise. These changes could be a short-term adaptation to noise levels (behavioural) or a long-term adaptation in populations due to average anthropogenic noise levels (genetic change, phenotypic plasticity). A species’ ability to adapt to anthropogenic noise may be a key factor in its success. It is therefore important to evaluate different species responses to noise for effective management. In this study I evaluated the effects of noise on Red-Winged Blackbird communication by assessing various parameters of their song when exposed and unexposed to noise in the vicinity of the Queen’s University Biological Station, Ontario, Canada. First, I compared the songs of Red-Winged Blackbirds located in quiet marshes and along the roadside, during quiet periods. This allowed me to test if the songs in the two areas differed in the absence of anthropogenic noise, which
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.712 | 0.586 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".