Special Enhancements Using NOAA Satellite Data to Evaluate Grasslands of Western Canada
Bibliographic record
Abstract
The grazing and hay lands of Saskatchewan, a Province in western Canada, extend from the wide open short grass prairie of the south to the northern roughland bush pastures of the Aspen Parkland (Populus tremuloides). These areas total 7,853,000 ha. This study is part of a continuing effort directed towards the development of operational remote sensing analysis techniques for the estimation of grazing conditions in both native and seeded pastures in Saskatchewan. Multi-spectral remotely sensed data from satellites has proven useful in assessing crop vigour. In most cases, where drought assessment or monitoring was required, large land areas were involved and high temporal resolution was preferred. A VHRR data from the NOAA series of satellites, with two passes per day over the critical areas, low spatial resolution at 1 km/pixel for large area coverage and two channels appropriate for vegetation analysis was found to meet these needs. Major species within the northern areas are bromegrass (Bromus inermis), intermediate wheatgrass (Agropyron intermedium), creeping red fescue (Festuca rubra), Kentucky bluegrass (Poa pratensis), alfalfa (Medicago sativa) and young aspen and balsam poplar (Populus balsamifera). Native vegetation on Mixed Prairie zone is composed of mixtures of western wheatgrass (Agropyron smithii), northern wheatgrass (A. dasystachyum), blue grass (Bouteloua gracilis), and sagebrush (Artemisia cana). Range improvement has included breaking and reseeding to wheatgrass, Russian wildrye (Elymus junceus), crested wheatgrass (Agropyron cristatum) and alfalfa (Medicago sativa).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".