Knowledge gaps at the intersection of road noise and biodiversity
Bibliographic record
Abstract
Roads are a ubiquitous source of noise pollution. Several recent reviews highlight the ecological and evolutionary consequences of anthropogenic noise, but do not specifically focus on roads. I leverage a prior systematic mapping effort to examine patterns in 183 studies of road noise on terrestrial plants and animals. Birds were the most studied taxon (62% of studies) followed by mammals (16%), amphibians (16%), insects (6%), reptiles (< 1%), arachnids (< 1%), and plants (< 1%). North America (USA and Canada) was site of the most in-situ studies (51%). Of the states and provinces in North America, a plurality of the studies were conducted in California (32%). The topic examined most often was communication (40%), followed by behavior (27%), and reproduction (22%). Most studies were experimental (54%) compared to observational (44%) and the proportion of experimental studies has increased yearly. The number of road noise studies published per year has increased over time along with the broader conservation literature. These results might suggest deprioritizing examinations of bird responses to road noise, particularly within North America. However, a plurality of those studies addressed communication, leaving a knowledge gap regarding physiology and space use. Effects of road noise on invertebrates, plants, and reptiles are severely understudied and research on such taxa would aid the management of natural resources.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".