Evidence of a union between organic carbon and water content in soil
Bibliographic record
Abstract
Soil organic matter, comprising ∼58% soil organic carbon (SOC), is attributed with increased water holding capacity in the surface horizon of agricultural soil. This paper addresses the role of SOC as a component of a common functional unit in soil from analysis within a single field and over multiple fields. Soil data measured on the fields during the SMAPVEX12 satellite prelaunch algorithm development campaign exhibited high correlation among SOC, field-mean soil water content (SWC), bulk density, and soil texture. The analysis extended over a wide range of soil texture and wetness in the top 5 cm of soil over 50 agricultural fields covering ∼400 km2 of southern Manitoba. Data collected over a much smaller area from Ontario silt loam soils at the Elora Research Centre demonstrated a similar correlation between SOC and SWC in intensive field sampling. This intercorrelation of SOC and SWC is examined with partial least-squares regression, principal component analysis, and geostatistical semivariograms. A model is proposed to interpret the feedback process between SOC and SWC to explain the persistent correlation. Further work to substantiate the strengths and limits of the relationship between SOC and SWC may be beneficial for estimating SWC for remote sensing, agriculture, hydrology, and ecosystem function.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".