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Record W151119821 · doi:10.15760/etd.1944

Behavioral Observation and Paternal Investment of Eastern Kingbirds at Malheur National Wildlife Refuge

2000· report· en· W151119821 on OpenAlexfundno aff
Christopher M. Chutter

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsWildlife refugeNest (protein structural motif)WildlifePopulationRiparian zoneGeographyEcologyHabitatFisheryDemographyBiologySociology

Abstract

fetched live from OpenAlex

We have been studying the population of Eastern Kingbirds breeding in riparian habitats in Malheur National Wildlife Refuge (eastern Oregon) since 2002. These efforts have created an ideal research environment wherein most adults in the population have been color banded and DNA sampled and, as part of other research projects, nearly all broods since 2003 have been paternity tested. I decided to use behavioral video recordings of parental nest behavior undertaken between 2003 and 2010 for two unrelated projects. First, I tested the effectiveness of video sampling nesting behavior (see below and chapter 2). Second, I tested whether male kingbirds were able to affect their level of paternal investment in accordance with their level or realized paternity (see below and chapter 3). Chapter 2 was split into three distinct questions: 1) are parental nesting behaviors repeatable, 2) is a one hour sample sufficient to capture variability in these behaviors, and 3) is the first hour of recording sufficient to capture variability in these behaviors. I found overwhelming evidence that the behaviors I measured were repeatable. This is truly important, for if repeatability was disproven, it would call into question the use of sampling throughout the field of animal behavior. I similarly found strong evidence that a one hour sample was sufficient to capture variability in parental behaviors. From this I was able to suggest that further sampling effort would be better spent increasing sample size rather than observation length. Testing whether the first hour of recording was sufficient to capture variability in parental behavior found more muddled results. While there generally was correlation between behavioral values in the first hour and those over a longer observation period, most behaviors were found to have significantly lower values in the first hour. I tested whether this was the result of a lingering observer effect or a natural effect of time of day and concluded that an observer effect was the more likely explanation. In chapter 3, I ran one of the more in depth and complicated tests for a relationship between paternal investment and realized paternity that I was able to find in the literature. I used the standard male feeding rate as a measure of male investment as well as a far more nuanced measure derived from the first Eigenvector of an analysis of six different paternal behaviors. These were both tested using Akaike's Information Criterion against a number of variables

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.069
GPT teacher head0.305
Teacher spread0.236 · 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
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

Citations0
Published2000
Admission routes1
Has abstractyes

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