The organization and variability of song in Northern House Wrens (Troglodytes aedon parkmanii)
Bibliographic record
Abstract
Hypothesized functions of complex song in birds include a role in mate attraction and territory defense and, through regional dialects, in genetic substructuring of populations and speciation.The necessary first step in testing such functions is a detailed characterization of song organization and variability.This is provided for the Northern House Wren (Troglodytes aedon), a species noted for complex song, but lacking detailed descriptions.The species was studied at two sites in Alberta with a sample of 15,000 songs from 15 males.Males sang in long bouts, each song composed of multiple syllable types and repeated many times before switching.The population repertoire of 27 syllables was almost entirely shared, but used to construct novel repertoires of up to 200 different song types for individual males without evidence of a ceiling.Additional flexibility and constraints in song construction are discussed in view of the above noted functions of song complexity.also to understand Canadian nature and wildlife, which was completely new for me.As a teacher, he contributed immensely for the development of my knowledge, and my academic and professional skills.His support for me to settle down in a foreign soil and to understand the Canadian culture is invaluable.I highly appreciate his continuous support, advice, and encouragement.I would also like to thank the members of my supervisory committee who have contributed to this research programme in many ways.Dr. Theresa Burg and Dr. Andrew Iwaniuk regularly provided feedback and advice on many aspects of the research.Dr. Burg supported me to enhance my knowledge on bird banding and related matters in the field.She also provided permission to use federal bands for House Wrens I banded in the field.In addition, I'm highly grateful to Karen Rendall for her support for this research and for me in many ways.She developed the database for an enormous dataset of songs v that continued to grow throughout the thesis.Her help for some data analysis through her knowledge on database management was invaluable.She also helped me to appreciate aspects of the new culture and nature in the field.I also thank all the faculty members of the Department of Psychology for their support in many ways throughout the period.I'm especially grateful to Leanne Wehlage-Ellis for her support for all the non-academic work.I would also like to thank my lab members, Brandon Yardy, Alan Nielson, Chantel Fouillard and Kyle Plotsky for their support in the lab as well as in the field.This section is incomplete if I don't mention my family at this moment.First I must thank my two little girls Piyumini and Sithumini for their tolerance and for agreeing to live without father for whole summer.I'm indebted to Soba for carrying the burden of family with two kids throughout the time, especially while enrolled in a graduate programme herself.I would like to thank my father and mother for understanding my never ending quest for knowledge and my passion for the nature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".