RFGRD_V1.0 computed reference grids for Canada west, north, offshore and Delphi NTv2 component
Bibliographic record
Abstract
Geographic reference grids are used to organize many types of spatially referenced information, including information about oil and gas exploration, development activities and lease holdings. As grid systems differ among jurisdictions, digital reference grids have been computed for British Columbia (BC Land grid); Manitoba, Saskatchewan, Alberta and the Peace River area of British Columbia (Dominion Land Survey grid); and Yukon, Nunavut and the Northwest Territories as well as Canada's offshore regions in the Atlantic, Arctic and Pacific Oceans (Canada Land grid). In addition a National Topographic System (NTS grid) has been computed for all of Canada that extends well into the offshore (40 to 84 degrees North, and 48 to 144 degrees West). Theoretically derived grids are presented as a single collection on DVD in geographic coordinates (i.e. not projected) in a commonly used data format, ESRI SHAPE files. The NTv2 National transformation is used to convert between NAD27 and NAD83 datums. The 2 Delphi NTv2 components extend the Geomatics Canada NTv2 Developer's kit, written in FORTRAN, to Delphi developers as components that can be dropped on a form to provide conversions using the Geomatics Canada NTV2_0.GSB or MAY76V20.GSB grid shift data. Note the latter is used in some Ontario basemaps. Executables use the NTV2.bpl or MAY76.bpl run-time library.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.152 | 0.074 |
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".