Gps performance in yukon's arctic coast
Bibliographic record
Abstract
This study analyses GPS performance in Yukon's Arctic coast to inform future research that uses this technology in the region. To test this, Telonics GPS collars were placed on stakes during the summers of 2009 and 2010 throughout the region for varying lengths of time. The fix records produced by these collars were then collected and cleaned, leaving 30 samples. Using these records combined with a digital elevation model, eight variables were extracted and analysed in an attempt to find relationships, such that a fix rate could be predicted throughout the landscape. The results indicated that very few strong relationships existed. Densiometer values proved to be the only relationship between an environmental variable and fix rate. Available sky and aspect data produced results that were contrary to those expected. Overall, Telonics Generation 3 collars had extremely high fix rates, high accuracy, and low positional dilution of precision. Moreover, there was little variation in these results. This means that future GPS studies in the region would likely require minimal correction for fix rate bias. However, if corrections were to be made, more data would have to be gathered to ensure the results were statistically sound. The analysis suffered from the limitations of small sample size and low sample variance, among several others. Therefore, future studies should increase the number and diversity of sites tested.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".