Bibliographic record
Abstract
Foreword This set of guidelines for Real Time Kinematic (RTK)/Real Time Network (RTN) Global Navigation Satellite System (GNSS) surveying has been prepared to assist the surveying community in Canada through sharing what we view to be best practices. The guidelines have been prepared in response to needs expressed by the Federal, Provincial, and Territorial members of the Canadian Council on Geomatics (CCOG). The work was coordinated by the Canadian Geodetic Reference System Committee (CGRSC), a sub-committee of CCOG, and the guidelines were developed by a team from different agencies, Federal and Provincial. A number of other individuals, agencies, and professional land surveying associations in Canada have also contributed to this effort, and we wish to thank them for improving this product. Although the authors strove for accuracy, please let us know if you find any errors or omissions and we will amend the text accordingly in future versions. Special thanks to Brian Donahue, Geodetic Survey Division; Jan Wentzel, Surveyor General Branch; and Ron Berg, Ministry of Transportation Ontario; for their leadership in completing this project, and to all the CGRSC members who provided valuable feedback throughout the process. We sincerely hope that these guidelines make a positive contribution to surveying in Canada.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.027 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".