Bibliographic record
Abstract
This research project, which is part of the Canadian GEOIDE (GEOmatics for Informed DEcisions) Network of Centres of Excellence, is dedicated to the improvement of the methodology and the algorithms to achieve more precise and more reliable kinematic GPS positioning which implies more reliable and efficient On-The-Fly (OTF) phase ambiguity resolution over distances up to 75 km, for the support of bathymetric surveys in real-time. To reach these goals, different research initiatives are considered, namely: i) GPS relative positioning with multiple reference stations, ii) the improvement of ionospheric modelling, iii) the use of precise real-time orbits, iv) the integration of Glonass observations and v) radio-communication management. The GEOIDE Network of Centres of Excellence started its research activities in March 1999. In this presentation, we will summarise research performed during the first year of our 3-year project. The results are mainly related to the use of a priori water level information (from tide gauges) to constrain the OTF-GPS solutions and the interpolation of relative ionospheric delays. Other activities conducted within our GEOIDE project will also be briefly discussed, namely, the development of improved algorithms for OTF ambiguity resolution and the processing of carrier phase observations from Glonass satellites.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.278 | 0.067 |
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".