An expert system for allocating soils to landforms through the application of soil survey tacit knowledge
Bibliographic record
Abstract
Soil survey is a paradigm-based science that relies heavily on the application of conceptual soil-landscape models, which in turn are based upon tacit pedological knowledge. This tacit knowledge is generally acquired by systematic field observation and recording the relationships between the occurrence of soils and associated landform positions. Soil survey databases identify the types of soils within a delineated area but they do not generally describe the relationship of specific soils with specific landscape positions. A case in point is the recently completed 1:100 000 scale soil landscape database prepared for the agricultural region of Alberta, Canada. In order to utilize this database with various interpretative algorithms a procedure for allocating soils to specific landform positions needed to be developed. The development of this procedure initially involved capturing the local tacit pedological knowledge in a series of tables and programs. The procedure was then applied to the Alberta soil survey database to automatically assign soils to landform positions and then to assign specific slope characteristics to the individual soils. The resulting soil-landform product was more useable than the original data for input to land based process models. Key words: Soil survey, tacit knowledge, soil-landscape modeling, heuristic rule base, predictive mapping
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".