No effect of passive integrated transponder tagging method on survival or body condition in a northern population of Black‐capped Chickadees (<i>Poecile atricapillus</i>)
Bibliographic record
Abstract
Abstract Passive integrated transponder (PIT) tags allow a range of individual‐level data to be collected passively and have become a commonly used technology in many avian studies. Although the potential adverse effects of PIT tags have been evaluated in several species, explicit investigations of their impacts on small (<12 g) birds are limited. This is important, because it is reasonable to expect that smaller birds could be impacted more strongly by application of PIT tags. In this study, we individually marked Black‐capped Chickadees (Poecile atricapillus), a small (circa 10 g) passerine, at the University of Alberta Botanic Garden to evaluate potential lethal and sublethal effects of two PIT tagging methods: attachment to leg bands or subcutaneous implantation. We used a Cox proportional hazards model to compare the apparent survival of chickadees with leg band (N = 79) and implanted PIT tags (N = 77) compared with control birds that received no PIT tags (N = 76) over the subsequent 2 years based on mist net recaptures. We used radio‐frequency identification (RFID) redetections of leg band PIT tags to evaluate sex‐specific survival and increase the accuracy of our survival estimates. We also used a generalized linear regression model to compare the body condition of birds recaptured after overwintering with leg band PIT tags, implanted PIT tags, or neither. Our analysis found no evidence for adverse effects of either PIT tagging method on survival or body condition. While we recommend carefully monitoring study animals and evaluating the efficacy of different PIT tagging methods, we have shown that both leg band and subcutaneously implanted PIT tags ethical means of obtaining individualized information in a small passerine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".