Better recognition of limnic materials at the great group and subgroup levels of the Organic Order of the Canadian System of Soil Classification
Bibliographic record
Abstract
In the Canadian System of Soil Classification (CSSC), soils of the Organic order are classified at the great group level primarily based on the dominant organic material in the middle tier. The system recognizes four types of organic horizons: fibric (Of), mesic (Om), humic (Oh), and coprogenous earth (Oco), of which only the latter is not recognized at the great group level of the Organic order. Furthermore, at the subgroup level, Limnic subgroups cannot have terric or hydric layers. This is problematic in soils where the middle tier is dominated by limnic materials, and those which have dominantly limnic materials and have a terric layer. We describe 29 soil profiles in Ontario and Quebec, which are either poorly captured in the CSSC or that cannot be classified into the Organic order based on their diagnostic criteria. Based on an analysis of soil survey information in five provinces across Canada, we estimate 32 057 ha of organic soils which potentially contain limnic deposits. In key vegetable-producing areas of Quebec, large organic deposits in agricultural production are subject to peat subsidence and erosion, resulting in shallower depths to underlying coprogenous earth, which is not a suitable medium for crop production. This can potentially have negative effects on crops when mixed with humic materials in the plow layer. Due to these taxonomic and agronomic considerations, we propose the addition of a new great group, Limnisol, and suggest further integration of limnic materials at the subgroup level for the Humisol, Mesisol, and Fibrisol great groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".