Bibliographic record
Abstract
Radiotelemetry is one of the most widely used techniques in wildlife research, and consequently methods and technologies are being improved constantly by users and manufacturers. For example, radiocollars are getting smaller and lighter, and include more options, and receiving systems can now operate from satellites. Methods for analyzing telemetry data also have changed: statistical methods are evolving, and so are computer systems. The result is that users who wish to remain at the cutting edge must stay on top of the literature, and Millspaugh and Marzluff's book can help. Radio Tracking and Animal Populations consists of 7 parts (Introduction, Experimental Design, Equipment and Technology, Animal Movements, Resource Selection, Population Demographics, and Concluding Remarks) and 15 chapters plus 1 appendix, authored by a total of 34 scientists. The diversity of authors is refreshing and leads to various perspectives on the same issues. Part I includes a single chapter (Kenward) that provides a good and very general overview and history of the development of radiotagging and telemetry. Part II goes into issues of concern for scientists, and 2 chapters discuss experimental design. The information presented is fairly basic (number of animals needed, number of locations per animal, etc.) but is very useful to reflect on before launching a study (e.g., effects of transmitters on animals, measuring and reporting location error). An appendix to Chapter 3 provides a good reference, by taxon and species studied, for articles that reported transmitter effects.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".