<i>Unrealistic</i> animal movement rates as behavioural bouts: a reply
Bibliographic record
Abstract
Summary Johnson et al. (Journal of Animal Ecology, 2002, 71, 225–235) proposed a technique for stratifying the movements of ungulates into small‐ and large‐scale behaviours. They identified movement paths for woodland caribou and fitted a nonlinear curve to the log‐frequency of movement rates. They assumed that slow small‐scale movements were correlated with foraging activities in patches and faster large‐scale movements occurred when caribou moved between patches. Nams (Journal of Animal Ecology, 2006, 75, 298–302) reviewed the assumptions and tested the technique presented by Johnson et al. (2002). Simulated animal movements resulted in rates inconsistent with the data of Johnson et al. (2002) and the distribution necessary to fit the nonlinear curve. Nams (2006 ) challenged animal movement as suitable for the technique and concluded that sampling interval would confound results. We evaluated Nams's (2006 ) criticisms with movement data collected for caribou, moose and mountain goat. All three species demonstrated the required distribution of movement rates and sampling interval had little influence on the criterion used to identify scales of movement for a range of woodland caribou data. In addition, we tested the sensitivity of the curve‐fitting model to the width of the frequency interval for the log‐frequency plot of movement rates. We noted bias in the rate criterion, but the scalar relationship was consistent among interval widths. The discrepancy in movement data presented by Nams (2006 ) and Johnson et al. (2002) is likely the result of different movement processes. The movements of simulated animals did not encompass the full range of behaviours typically observed for ungulates. Our analyses and those of Nams (2006 ) provide little evidence to universally reject the nonlinear curve‐fitting model and the results of Johnson et al. (2002). However, we caution against blind application of the technique, as not all movement processes are suitable and the scale of movement must be consistent with the scale of the behaviour.
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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 | 0.033 | 0.135 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.033 | 0.075 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".