What they do in the shadows : stable isotope analysis reveal that spatial and temporal heterogeneity explain dietary niche variation in Myotis lucifugus in Newfoundland
Bibliographic record
Abstract
Individuals must balance competitive and environmental pressures with obtaining the nutrients necessary to survive and reproduce.The goal of this project was to infer on individual dietary adaptations of adult female Myotis lucifugus from a maternity group.Therefore, I conducted stable isotope analysis on feces (n = 127), arthropods (n = 110), and hair (n = 120) collected from known individuals across two timescales (feces sampled May-August 2017; hair sampled 2012-2017).I used a Bayesian mixing model (MixSIAR) and an information-theoretic approach to determine models that best explained variation in isotopic niche.Isotopic niche variation across both timescales was strongly explained by spatial and temporal heterogeneity, with little explanatory power provided by inter-individual or reproductive group heterogeneity.Diets of individual bats were opportunistic, with strong dependence on the most abundant prey groups, although diets of most individuals contained a limited amount of all prey groups.throughout the entirety of the project.I thank you for both your partnership in this project and throughout the rest of our lives together.Without your unrelenting support and encouragement, this project would not have been possible.I was assisted in the stable isotope analysis by Phillip J.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".