Analysis of GPS and Loran-C performance for land vehicle navigation in the Canadian Rockies
Bibliographic record
Abstract
Loran-C and GPS (Global Positioning System) were assessed for vehicular navigation along selected roads of British Columbia during the winter of 1991. The general topography of this mountainous area is described, together with the specific topographic features and tree coverage characteristics of the 2000 km of roads tested on the mainland and on Vancouver Island. The configuration and characteristics of the Loran-C Canadian West Coast chain along the roads used are described. The portable vehicle-mounted LORCAL (Loran Calibration at the University of Calgary) system used to collect and analyze the Loran-C and GPS signals along road profiles is described. The performance of Loran-C is analyzed in terms of signal to noise ratios, field strength, and time difference distortions, as measured by differential GPS. These distortions, which can reach several hundred meters over distances of less than 20 km, are analyzed in terms of topographic features. The possibility of using these time-independent distortions to calibrate Loran-C for use along the above roads is discussed. Masking of GPS signals due to topographic features and tree coverage along the roads is analyzed. A comparative analysis of Loran-C and GPS is presented in terms of signal availability and accuracy.>
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".