Comparative Assessment of Remote Sensing and Holos™ Based Soil Organic Carbon Stock Estimation
Bibliographic record
Abstract
Soil organic carbon (SOC) is a critical component of soil health, influencing climate regulation and sustainable land management. Accurate estimation of SOC stocks using remote sensing (RS), especially across different soil depths, remains challenging due to spatial variability and methodological uncertainties. This study assesses SOC stocks at a 30 cm depth using three remote sensing-based approaches (RS1, RS2, RS3) along with HolosTMmodel and AgExpert model across fields in Saskatchewan, Canada. Results indicate that RS3 showed the closest alignment with (HolosTM) and AgExpert, achieving RMSE values of 2.54 t/ac and 2.84 t/ac, and mean differences of 1.23 t/ac and 1.66 t/ac, respectively. RS1 showed moderate agreement, while RS2 exhibited the highest variability due to generalized conversion factors from other geographies. Annual carbon stock change trends reveal stability from 2020–2021, followed by significant variability in 2022, particularly for fields F4, F5, and F6. This work emphasizes the need to address surface SOC uncertainties and integrate complementary approaches to improve SOC stock estimation for carbon management and climate action.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".