Automated tracking of wild hummingbird mass and energetics over multiple time scales using radio frequency identification (RFID) technology
Bibliographic record
Abstract
We examined the feasibility of automating the collection of hummingbird mass data facilitated by low‐cost, low‐power radio frequency identification (RFID) technology. In a field study in southern Ontario, wild hummingbirds were captured, subcutaneously implanted with passive integrated transponder (PIT) tags, and released over a three‐year period. Tagged hummingbirds were detected at specially designed feeder stations outfitted with low‐cost, low‐power RFID readers coupled with a perch secured to a digital balance. When tagged birds visited the feeder, transponder detection initiated the recording of the perched hummingbird's mass at regular intervals continuing as long as the bird remained. This permitted a nearly continuous record of mass during each visit. Mass data collected from tagged hummingbirds showed consistent trends at multiple temporal scales: the individual feeder visit, single days, and even whole seasons. These results further confirm that RFID technology is safe for use in the smallest birds. The effective detection range is a function of RFID reader power, antenna, and tag size. Yet, we find that careful arrangement of feeders and detectors allows for reliable detection even when detection range is low. When coupled with additional technologies, such as a precision electronic balance, this approach can yield robust serial measures of physiological parameters such as mass, an indicator of energy balance over time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".