Rhythmic properties of the flight song of the Skylark, Alauda arvensis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".