Developing statistical models to estimate the carbon density of organic soils
Bibliographic record
Abstract
Carbon density is a key variable in assessments of local or regional soil carbon (C) stocks, but its direct measurement on large numbers of samples is both time-consuming and expensive. To assess whether the C density of organic soils can be inferred from other parameters, we examined the ability of field- (stratigraphic depth and material type) and lab- (bulk density and ash content) based variables to predict the C density of organic soil samples. Candidate models given three different levels of a priori information about samples were developed from data for continental western Canada and examined using Akaike’s information criterion (AIC). Models at each level were then used to predict profile-level C storage in cores from three different regions (continental western Canada, Ontario, and the Northwest Territories). In profiles from western Canada, predictions were unbiased, with mean prediction errors of 0–7% and local precision depending on the amount of a priori information available. Application of models to other regions yielded mixed results, probably reflecting both differences in site characteristics and classification/analytical methods used. Since these error sources are impossible to separate given available data, we recommend that models for C density prediction should be tailored to a given research question and region. The results suggest that simple, field-based variables are sufficient to predict C density for the purpose of regional surveys. To obtain accurate estimates at the profile level, bulk density (and ash or C content) have to be measured in the lab. Key words: Soil (organic), carbon density, bulk density, ash, organic matter, models (predictive)
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".