A look inside the net: using social network analysis to investigate freshwater turtle feeding aggregations
Bibliographic record
Abstract
Sociality, the tendency for animals to live in groups, is influenced by ecological and evolutionary processes at every level of organization.In vertebrates, sociality exists as a spectrum, not a dichotomy.Despite this, non-avian reptiles are often incorrectly assumed to be non-social.I investigated signatures of social structure in feeding aggregations of three species of freshwater turtles caught in baited traps.I found that turtles were significantly clustered spatially and temporally in traps and were positively assorted with their conspecifics.Of the species studied, painted turtles (Chrysemys picta) were most often co-captured with other individuals.Furthermore, these turtles showed no evidence of intraspecific sex assortment and no withinspecies relationship between an individual's body size and the number of co-captures.These results expand our understanding of turtle sociality, can further our understanding of the ecology and evolution of sociality in vertebrates, and highlight avenues for future research.I extend tremendous gratitude towards my supervisors, Dr. Roslyn Dakin and Dr. Christina Davy, for their enthusiastic and patient mentorship throughout this journey.I will be forever grateful for your guidance and support through this project's peaks and valleys.To Roz, thank you enormously for your dedication to helping me overcome the R statistics software learning curve!To Christina, thank you enormously for the field experience (and time spent outside) you have given me!Thank you to Dr. Tom Sherratt, who started as my co-supervisor and
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".