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Record W2103359691 · doi:10.1093/czoolo/56.4.454

Convergences in the diversification of bats

2010· article· en· W2103359691 on OpenAlexaff
M. Brock Fenton

Bibliographic record

VenueCurrent Zoology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsHuman echolocationBiologyZoologyMammalEvolutionary biologyEcologyNeuroscience

Abstract

fetched live from OpenAlex

Abstract Twenty-five characters or suites of characters from bats are considered in light of changes in bat classification. The characters include some associated with flower-visiting (two), echolocation (12), roosting (six), reproduction (two) and three are of unknown adaptive function. In both the 1998 and 2006 classifications of bats into suborders (Megachiroptera and Microchiroptera versus Yinpterochiroptera and Yangochiroptera, respectively), some convergences between suborders are the same (e.g., foliage roosting, tent building), but others associated with echolocation differ substantially. In the 1998 phylogeny convergences associated with echolocation (high duty cycle echolocation, nasal emission of echolocation calls) occurred among the Microchiroptera. In the 2006 phylogeny, they occur between Yinpterochiroptera and Yangochiroptera. While some traits apparently arose independently in two suborders (e.g., foliage-roosting, tent building, low intensity echolocation calls, noseleafs, nasal emission of echolocation calls, high duty cycle echolocation behaviour), others appear to have been ancestral (roosting in narrow spaces, laryngeal echolocation, stylohyal-tympanic contact, oral emission of echolocation calls, and small litter size). A narrow profile through the chest is typical of bats reflecting the thoracic skeleton. This feature suggests that the ancestors of bats spent the day in small crevices. Features associated with laryngeal echolocation appear to be ancestral, suggesting that echolocation evolved early in bats but was subsequently lost in one yinpterochiropteran lineage.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.258
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2010
Admission routes1
Has abstractyes

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