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Record W6921326477 · doi:10.7479/m0ge-6a51

Rhythmic properties of the flight song of the Skylark, Alauda arvensis

2021· dataset· en· W6921326477 on OpenAlexaff

Bibliographic record

VenueMuseum für Naturkunde Berlin - Leibniz-Institut für Evolutions- und Biodiversitätsforschung · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsMinnow Environmental (Canada)
Fundersnot available
KeywordsRhythmDuration (music)TimestampSound productionBioacousticsElement (criminal law)Variation (astronomy)

Abstract

fetched live from OpenAlex

The dataset comprises the Skylark flight song data of the project "Novel ideas to further expand the applicability of rhythm analysis " written by Burchardt, L.S.; Briefer, E. F. and Knörnschild, M. Time information on song elements of 14 flightsong sequences of 14 Skylark males are included. The songs were recorded by E. F. Briefer and were originally published here: Briefer, E., Rybak, F., Aubin, T. (2008). When to be a dear enemy: flexible acoustic relationships of neighbouring skylarks, Alauda arvensis. Animal Behaviour, 76, 1319-1325. doi:https://doi.org/10.1016/j.anbehav.2008.06.017 and Briefer, E., Rybak, F., Aubin, T. (2010). Are unfamiliar neighbours considered to be dear-enemies? PLoS One, 5(8), e12428. doi:https://doi.org/10.1371/journal.pone.0012428 Timestamps of the analyzed sound sequences are given, namely the start and the end of sounds. Furthermore, the corresponding ID (i.e. sl01 for Skylark 01) and IOIs (Inter-Onset-Interval, the duration between the start of one element to the start of the next element in the sequence) are given. The data was used to test new methods of rhythm analysis in animals' acoustic signals, especially to develop the universal goodness-of-fit value ugof. Rhythm analyses that were run on the data include Fourier analysis and IOI analysis as well as the visualisation with recurrence plots.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.021
GPT teacher head0.255
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2021
Admission routes1
Has abstractyes

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