Proposed revision to Canadian System of Soil Classification: broaden taxonomic criteria for applying LFH horizons to include nonforest soils
Bibliographic record
Abstract
In the first edition (1974) of Canadian System of Soil Classification (CSSC), the taxonomic criteria for LFH organic horizons allowed application to any soil and land use developed under imperfectly to well-drained conditions. However, in the third edition (1998) of CSSC, the narrower taxonomic criteria for LFH horizons restricted application to only forest soils. A limited survey was conducted of some soil scientists across Canada to ask them if they had observed LFH horizons in nonforest soils. Distinct LFH horizons were observed across Canada under agriculture such as in no-till fields, tame and native pastures, and in reclaimed soils. They have also been observed in urban areas such as golf courses and grass-recreation fields. LFH horizons could also potentially develop under other nonforest land uses across Canada. Since no-till and native and tame pastures are most dominant in the prairies, the potential for LFH horizons is greatest in this region than elsewhere. However, they may occur anywhere in Canada where accumulation exceeds decomposition of organic material and they contain more than 17% organic carbon by weight or 30% organic matter. Therefore, we propose that the taxonomic criteria for applying LFH horizons be revised and broadened to include nonforest soils and be applicable to any soil order (where relevant) within Canada, and be at the discretion of the field pedologist. It is critical to identify and monitor LFH horizons over time because they are important for soil health, climate change, greenhouse gases, carbon sequestration, nutrient cycling, soil erosion, and hydrology.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".