Bibliographic record
Abstract
The evolutionary drivers responsible for the selection of high-frequency hearing in teleosts remain unclear. It is generally accepted that low-frequency hearing is the basal (plesiomorphic) condition, with specializations evolving to allow expansion of hearing bandwidth. While selective forces, usually habitat-based, have been proposed to explain the evolution of high-frequency hearing, phylogenetic analysis is currently lacking. The current study examines all available teleost hearing abilities in relation to habitat parameters, salinity, and maximum depth of occurrence of each species. There was no statistical correlation between any of these parameters and maximum frequency of detection and only a weak relationship to best frequency. Phylogenetic position, at the subdivision/superorder level, did significantly predict both maximum and best frequency of detection, but there was no consistent pattern of high-frequency hearing within the Teleostei, suggesting independent evolution of this ability. These trends were also consistent at the family level. The current reanalysis of available data therefore suggests little evidence for the habitat-based hypotheses of high-frequency hearing evolution in the Teleostei. Interesting families and approaches will be highlighted in the current talk in an attempt to foster a more systematic approach to future studies of hearing in fish. [Work supported by NSERC.]
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".