Land Use and Cover with Intensity Assessment for Canada from Satellite and Census Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we describe a new system for the construction of both land use and land use intensity (within agricultural regions) called LUCIA (land use and cover with intensity of agriculture). Our methodology combines the highly detailed Canadian Census of Agriculture and recent growing season composites derived from the SPOT4/VEGETATION sensor. Census data are of much coarser resolution than the remotely sensed data, but, by removing non-agricultural pixels from each census sampling area, we were able to spatially refine the census data sufficiently to allow it to be used as ground truth data in some areas. The refinedcensus data were then used in the final step of an unsupervised classification of the remotely sensed data. <p>The results of the land use classification are generally consistent with the input census data, indicating that the LUCIA output reflects actual land use trends as determined by national census information. Land use intensity, defined as the principal component of census variables that relate to agricultural inputs and outputs (e.g. chemical inputs, fertilizer inputs, and manure outputs), is highest in the periphery of the great plains region of central Canada but is also very high in southern Ontario and Québec.
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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 it