Prediction of organic carbon content in upland forest soils of Quebec, Canada
Bibliographic record
Abstract
Soil organic carbon (C) is an important component of forest carbon pools and should be taken into account in sustainable forest management. However, there is a need to derive indicators for this attribute, as organic C content (Mg·ha1) in forest soils is generally not available in Quebec survey data. Thus, we developed models to predict organic C accumulation in the forest floor and in mineral soils of upland forest soils (i.e., with forest floor thickness [Formula: see text]30 cm) using soil survey data as input variables. The best-fit model for predicting forest floor organic C content consisted of the explanatory variables forest floor thickness, latitude, and longitude. The model R2 was 0.76, and its CV was 28%. The second best-fit model, excluding geographical coordinates, included forest floor thickness and mean growing season precipitation as explanatory variables (R2 = 0.71, CV = 29.5%). The model for predicting mineral soil organic C content was composed of two submodels: (i) organic C concentration of a mineral horizon as a function of its colour and (ii) bulk density of that horizon as a function of its estimated C concentration (obtained from the former model). The R2 of the model for predicting organic C content in mineral soils was 0.57, and its CV was 29%. The models were used to predict organic C contents in 5547 pedons, dispersed throughout the commercial forest of Quebec and for which basic soil profile description data were available. It was then possible to evaluate and compare mean soil C accumulation in different forest stand types and to construct two maps of soil organic C accumulation in upland forest soils of southern Quebec. The results pointed out that forest floor thickness combined with mineral soil horizon colour, texture class, and pH would be useful sustainable forest management indicators of organic C accumulation in upland forest soils.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".