Changes in ornithological methods in the past 33 years
Bibliographic record
Abstract
Changes in ornithological methods in the past 33 yearsThe Editors Radio telemetry involves tracking animals using transmitters that emit pulses on very high radio frequencies.The technology allows researchers to track many different individual animals with high temporal and geographic precision.Radio-telemetry has played an important role in research and conservation on a wide variety of taxa for over 60 years (Adams 1965, Cochran et al. 1965).In recent decades, automation of receivers, miniaturization and digitization of tag signatures and coordination of monitoring efforts, have allowed researchers to simultaneously track larger numbers of individuals at broader scales than previously possible.The Motus Wildlife Tracking System (Motus is Latin for 'movement') is a cooperative automated radio telemetry system that harnesses the collective power of many researchers and organizations into a globally coordinated effort that expands the scale, scope and impact of everyone's work.Motus is a not-for-profit program of Bird Studies Canada (BSC) in partnership with Acadia University and other collaborating researchers and organizations.It is funded through a combination of user fees and major support from various government agencies and private foundations.The core operations of Motus were initially supported by the Canada Foundation for Innovation, through a grant to Western
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".