Soil Zones of the Canadian Prairies: Creating Art to Visualize the Concept
Bibliographic record
Abstract
Agriculture students, soil science faculty and farmers are familiar with the popular soil zone concept map for the Canadian Prairies. The map, first depicted by Professor AH Joel back in 1928 was based on organic matter contents as affected by climate and parent materials. Newer versions of the soil zone map have been created with colors associated with each soil zone (Brown, Dark Brown, Black, Gray and Dark Gray) that most people would be familiar with today. However, even though we have this mental picture of the color associated with the soil zones, what do the soils really look like if one were to visit sites in each of the different soil zones? Thus, the objective of the project was to collect surface soils samples from N-S transects along three highways in the province of Saskatchewan to convey the soil zones visually through art. Soil samples were collected every 25 km along the three highway transects: one in the east (Highway 9), one in the middle (Highway 2) and the west side of the province (Highway 21). Soil samples were dried, ground and sieved and then the samples used to create soil rubbings on watercolor paper for each of the transects. These transects would then be hung in the College of Agriculture building. A booklet would be developed with QR codes identifying where the samples were collected (GPS, nearest town, land management) and the organic matter content of the soils (measured in the soil science laboratory) that would be used for educational purposes whether in our soil science labs, lectures or summer children camps. This presentation will highlight the development of this project and how the information was used to visually communicate to students and the public the science behind the soil zones of the province.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".