Mapping and modelling a boreal forest soil organic carbon predictor in the glacial till of Newfoundland, Canada
Bibliographic record
Abstract
Abstract. Boreal forest soils store 30 % of global forest soil carbon, making them a crucial component of the carbon cycle. However, climate change at high latitudes is resulting in heightened temperatures and increasingly unpredictable precipitation patterns. Soil organic carbon (SOC) formation and stabilization is tied to precipitation patterns, thus climate change will inevitably influence the stability and longevity of boreal forest SOC. The current size and distribution of this reservoir is poorly understood, creating uncertainty under current and future climate scenarios. Previous research demonstrates mineral soil properties may be used to model boreal forest SOC accurately. The surface slope, depth of carbon enriched horizon, and climate characteristics are important parameters for modelling SOC and can generally be obtained or estimated via remote sensing. However, information about aluminum availability – the weatherable aluminum capable of interacting with organic matter to form stable carbon rich organometal complexes in mineral soils – is not widely available but is controlled by soil parent material. To bridge this gap, the Newfoundland and Labrador till geochemistry dataset was used here to map and model aluminum availability in glacial till across the island of Newfoundland as a function of geology and climate. The Random Forest Algorithm was employed to develop two models: one relying solely on the strength of geological and climatic variables, and the other drawing additionally on the spatial context of sample points. The first model performed well (R2=0.60), however, adding a spatial component increased the performance of the second model (R2=0.71). Bedrock type and proximity of the samples to certain units were indicated to be the strongest controls on aluminum availability, while environmental factors were less influential. Additionally, model uncertainty was calibrated empirically and mapped spatially, providing reliable and actionable information about the confidence of predictions over the study area. This project demonstrates the value of predictive geospatial modelling for till geochemistry mapping and delivers key aluminum availability predictions for deriving SOC reservoir estimates across Newfoundland.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".