GPS measurement error and resource selection functions in a fragmented landscape
Bibliographic record
Abstract
Advances in technology have allowed ecologists to employ remote observations of individual organism's spatial location. These data are used to model species distributions and habitat associations, which inform conservation efforts and management plans. These data are not without error. To illustrate the consequences of not considering measurement error, I introduce measurement error to a habitat selection model, using three different distributions. I show how measurement error can confound inferences made about a hypothetical organism's true habitat selection. By simulating different initial strengths of selection I show the introduction of measurement error results in the largest reduction in habitat selection strength (from truth) for very selective individuals (habitat specialists). Not surprisingly, the inclusion of error in very weakly selective individuals (habitat generalists) can result in a switching from true selection to observed avoidance. Researchers need to be aware that, first, there is measurement error in remotely observed data, and second, a tradeoff occurs between measurement error and landscape fragmentation. Landscapes with a high degree of fragmentation require spatially accurate (low measurement error) data in order to make reliable estimates of habitat selection or species distribution. The results of this study are discussed in light of the conservation of species threatened by habitat fragmentation and the management suggestions arising from selection studies.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".