Bibliographic record
Abstract
In general, a Reference Station calculates differential corrections which are valid for that exact location (zero baseline) at that particular epoch (age of corrections zero). However, DGPS users may be located as far as 200 nm away from the Reference Station and some of the errors compensated for by the Reference Station vary with space, namely satellite ephemeris, tropospheric and ionospheric errors. Therefore, the corrections calculated at the Reference Station suffer certain accuracy degradation as the separation distance increases, because of a decreasing relevance of the Reference Station data to the user. The error growth with increasing distance to the beacon is accentuated by the inability of Reference Station and user to see the same satellites, commonly termed the lack of intervisibility. The error growth with distance is the most important factor determining DGPS accuracy, but surprisingly very little has been done to assess it. US official documents and IALA state that the achievable accuracy degrades at an approximate rate of 1 m for each 150 km (80 nm) distance from the broadcast site, but this value is based on a theoretical prediction, made back in 1993. To estimate the error growth with real data, 6 DGPS receivers were placed along the Portuguese coastline at approximately 50 nm intervals from Sagres Broadcast Station, in a South – North direction. This paper describes the results of the trial.
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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 | 0.024 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".