Bibliographic record
Abstract
The Centre for Topographic Information (CTI) of Natural Resources Canada is currently producing a complete set of cloud-free orthoimages covering the Canadian land mass using data from the Landsat-7 satellite (under a project called Ortho-7). The project is being undertaken in partnership with GeoConnections, the Canada Centre for Remote Sensing (CCRS), provincial and territorial agencies, and other federal government departments. In addition to financial support, partners are providing topographic control data to assist in producing orthoimages of high quality and accuracy. The creation of a national coverage with Landsat-7 orthoimages will provide an up-to-date fundamental geospatial framework for Canada. These products will serve as an excellent reference for map updating, and their geometric integrity will facilitate data integration from other map and image sources. The inherent information content of the imagery will also serve as a rich baseline for characterizing the Canadian land mass. Image acquisition for this initiative began in 1999 and will continue until complete coverage of Canada is obtained (scheduled for completion in 2004). Of the estimated 750 scenes required to cover the Canadian land mass, 400 images have already been identified as suitable for production. The primary criterion is that the imagery must be cloud and haze free. The ortho-correction is being done in partnership with Canadian industry and is proceeding as scheduled. This note is intended to provide details about the Landsat-7 orthoimage data specifications, production, and delivery model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".