Land cover classification and assessment of carrying capacities and stocking rates of crown lands in Manitoba
Bibliographic record
Abstract
Understanding the carrying capacity and the stocking rates of crown lands is critical for the beef industry in the Prairies that relies heavily on these lands for grazing. The overall goal of this study was to examine the current carrying capacities and stocking rates of the crown lands in Manitoba. The main objectives of this study were to i) classify each crown land parcel in the province by land cover type or vegetation type and ii) estimate the carrying capacities and stocking rates of each parcel and compare these to the current stocking rates allowed by the provincial crown land leases. This study used remote sensing and geographic information system (GIS) technologies for land cover monitoring and estimation of carrying capacities and stocking rates. Based on the assessment of remotely sensed land cover inventories, forest and shrubland were found to be the dominant land cover types in the crown lands compared to native and tame grasslands, which are more desirable for grazing due to higher forage quality and palatability. Then, the carrying capacities were estimated from past field surveys that measured forage productivity in different ecoregions of Manitoba. The carrying capacities were used to calculate the stocking rates based on the delineated land cover types within each crown land parcel. Results show that the current stocking rates of the majority of the crown lands were lower than the estimated stocking rates. This suggests that these parcels were being undergrazed compared to the current grazing intensities permitted by the lease contracts. Overall, the forage resources of the crown lands in Manitoba were being undergrazed by -44.64%. This study can contribute to the existing management of crown lands and it also demonstrated the potential of remote sensing technology to improve and expedite land cover monitoring and stocking rate estimation for crown land managers in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".