Data from: Predictive modeling of ecological patterns along linear-feature networks
Bibliographic record
Abstract
Ecological patterns and processes often take place within linear-feature networks, and this has implications when analysing the spatial configuration of such patterns or processes across a landscape. One such pattern is the use of landscapes by human recreationists: an important variable in animal habitat selection and behaviour. Due to the difficulty in obtaining data, ecologists tend to use coarse metrics such as linear-feature density, while the extent and timing of human activity are often ignored. Remote detector equipment and its increasing use in ecological studies allow for large volumes of data on human activity to be collected. However, the analysis of these data still can be challenging. Using a combination of generalised linear mixed-effects models and network-based ordinary kriging, we developed a method for estimating spatial and temporal variations in motorised and non-motorised activities across a complex linear-feature network. Trail cameras were set up between 2012 and 2014 and monitored motorised and non-motorised activities at 238 different trail sites across a 2824 km2 region of the eastern slopes and foothills of central Alberta's Rocky Mountains. We evaluate the predictive capacity of this approach, demonstrate its application and discuss its merits and limitations. This method offers a straightforward analysis that can be applied to remotely acquired data to give a useful metric for assessing wildlife responses to human activity, and has potential application beyond the highlighted example.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".