Associations on land and at sea? A pilot study on the utility of proximity loggers to assess inter-individual relationships in colonial seabirds
Bibliographic record
Abstract
Accurate and extensive data collection is essential for understanding animal sociality, but collecting associations between individuals remains challenging. Animals often associate and interact outside of the range of an observer, especially in environments such as underwater or underground. However, the development of proximity loggers using Bluetooth and radio frequency to detect associations allows scientists to access behavioural information that would otherwise be impossible to collect. Here we examined the use of a logger with a proximity feature to capture associations between Atlantic puffin individuals and assessed how it could complement observations social network studies. To understand the capabilities of the logger, we tested the effect of distance on signal strength and proportion of associations detected, as well as the proportion of contacts recorded by each logger in a dyad, in lab-based and field environments. Thereafter, we tested the loggers on live Atlantic puffins and compared their performance against visual observations. As expected, signal strength decreased with distance, and lab-based values were more consistent than in the field. The proportion of contacts successfully processed decreased with distance, but our experiment in the field was more reliable, probably because we used a lower logger density, limiting opportunities for interference among units. More importantly, the loggers identified more putative associations than detected by observations, including many when and where individuals were not under observation. We also demonstrate that Atlantic puffins that associate frequently on land also associate frequently at sea. Our results bring new insight into the understanding of Atlantic puffin social behaviours, particularly at times and in locations challenging to monitor.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".