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Record W2790701798 · doi:10.1007/s00359-018-1250-8

Listening in the bog: I. Acoustic interactions and spacing between males of Sphagniana sphagnorum

2018· article· en· W2790701798 on OpenAlexaff
Glenn K. Morris, Aaron M. Hall, Heiner Römer

Bibliographic record

VenueJournal of Comparative Physiology A · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of Toronto
FundersKarl-Franzens-Universität Graz
KeywordsSympatric speciationBogAcousticsMode (computer interface)EcologyBiologyPhysicsComputer science

Abstract

fetched live from OpenAlex

Males of the katydid Sphagniana sphagnorum form calling aggregations in boreal sphagnum bogs to attract mates. They broadcast frequency-modulated (FM) songs in steady series, each song comprised of two wing-stroking modes that alternate audio and ultrasonic spectra. NN analysis of three populations found mean distances between 5.1 and 8.4 m, but failed to find spacing regularity: in one males spaced randomly, in another they were clumped, but within the clumps spaced at random. We tested a mechanism for maintaining inter-male distances by playback of conspecific song to resident males and analysing song interactions between neighbouring males in the field. The results indicate that the song rate is an important cue for males. Information coded in song rates is confounded by variation in bog temperatures and by the linear correlation of song rates with temperature. The ultrasonic and audio spectral modes suffer different excess attenuation: the ultrasonic mode is favoured at shorter distances (< 6 m), the audio mode at longer distances (> 6 m), supporting a hypothesized function in distance estimation. Another katydid, Conocephalus fasciatus, shares habitat with S. sphagnorum and could mask its ultrasonic mode; however, mapping of both species indicate the spacing of S. sphagnorum is unaffected by the sympatric species.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.900
Threshold uncertainty score0.185

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 head0.060
GPT teacher head0.376
Teacher spread0.316 · 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 designBench or experimental
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

Citations11
Published2018
Admission routes1
Has abstractyes

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