Influence of filtering methods on home-range size estimations.
Bibliographic record
Abstract
<p>We present (A) the average home-range size, (B) the size ratio Argos to GPS, and (C) the proportion of Argos locations found in GPS home-range size estimates calculated while estimating home ranges based on Argos and GPS locations from mobile experiments using 95% MCP and 95% kernel (h = 850, cell grid size = 250m). The home ranges were estimated for the following scenarios: raw data (Raw), only LC3 and LC2 locations (LC32), LC3 and LC2 and LC1 locations (LC321), data filtered with the Douglas Argos filter (DAF), data filtered with a homemade speed filter (HSF), simulated Argos locations with errors from the static error distribution (Static), and simulated Argos locations with errors from the mobile error distribution (Mobile). Home-range size (A) using the GPS reference area (Ref) is also shown. Data were obtained from Argos Platform Terminal Transmitters deployed simultaneously with GPS receivers on Bylot Island, Nunavut, Canada in July 2012.</p>
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".