Comparing radio-tracking and visual detection methods to quantify group size measures
Bibliographic record
Abstract
Abstract 1. Average values of animal group sizes are prone to be overestimated in traditional field studies because small groups and singletons are easier to overlook than large ones. This kind of bias also applies for the method of locating groups by tracking previously radio-collared individuals in the wild. If the researcher randomly chooses a collared animal to locate a group to visit, a large group has higher probability to be selected than a small one, simply because it has more members. 2. The question arises whether location of groups by means of finding collared animals has smaller or greater bias than searching for groups by visual observation. If the bias is smaller or same, this method can be recommended for finding groups. However, such a comparison cannot be made by speculation, only by empirical investigation. 3. The present study compares the two methods empirically, by statistically comparing group size measures (mean, median, quantiles, frequency distribution, and ‘typical group size’) between two data sets. These data sets comprise of Rocky Mountain mule deer group size values collected in the same area during the same period of time, referring either to groups located by the traditional ‘search and observe method’ or located by tracking formerly collared individuals. 4. All group size measures are statistically similar in the two samples, thus we conclude that the two methods yielded similar biases. Although the true group size measures are not known, we presume that both methods have overestimated them. We propose that these results do not necessary apply to other species, thus cannot be generalized. The reason for this is that bias may depend on factors specific to the species: bias of visual observation may depend on how well the species conceals itself in the existing habitat, and the bias associated with finding groups using collared animals is likely dependent on group size distribution and also on the proportion of collared animals in the population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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) | 0.000 | 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 teacher head, 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".