The Animal Locator: a new method for accurate and fast collection of animal locations for visible species
Bibliographic record
Abstract
Ranging behaviour is one of the most important aspects of the life history of many species. Spatial distributions of individuals in the wild is linked to factors such as foraging, mating, population density, availability of resources and competition. Accurate data on the spatial location of individuals over time is often difficult to collect. Here, we propose a new simple, non‐invasive and economic method for collecting accurate spatial data usable for many different species of free‐ranging animals. Our instrument for collecting animal locations consists of three elements: a laser range finder, a laser tilt sensor and a protractor. This instrument can obtain three‐dimensional parameters of the space from a fixed point allowing the user to collect geographical locations of the animals and, in general, of any point of interest. The device we tested showed a very low average error among (1.76 ± 0.643 m) and within (1.79 ± 0.058 m) observers, and the locations we obtained were all within the 95% probability of the tolerance intervals for the 20 positions which we measured repeatedly with a Global Position System for each of 10 different test locations. We tested a range of different distances to the target points (from 20 to 222 m), and we propose formulas to calculate precision of the instrument inside this interval. Precision of estimated locations was between 0.32 to 3.55 m from the real location and it was slightly related to distance of the target point (r = 0.38, P = 0.054). As an example of its practical application, we present data on the use of the instrument within the framework of a study on a population of free‐ranging individually tagged alpine marmots Marmota marmota .
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".