Progress Toward Remote Sensing of Soil Organic Matter Quality (Extended Abstract)
Bibliographic record
Abstract
In the past 30 years, the arctic climate has warmed appreciably and there is evidence for a significant polar amplification of global warming in the future.A warming and drying of northern soils could result in an increase in organic matter decomposition and positive feedback to future climate warming.Northern ecosystems have accumulated 25-33% of the world's soil carbon, much of which is preserved as poorly decomposed plant remains.Soil organic matter (SOM) decomposition rate, however, depends on many variables such as temperature, nutrient availability, pH, oxidation/reduction potential, and chemical composition of the SOM.This paper addresses the effect of SOM composition on CO, respiration in arctic soil under substrate-limited conditions.In previous research, the dependence of CO, respiration on SOM composition for arctic soils was empirically modeled for substrate-limited conditions.Soils from across the Western and Northern Alaska transects were analyzed in an attempt to tie subsurface SOM quality to cover type, a parameter that could be remotely sensed.The three cover materials studied were moist acidic tundra, moist non-acidic tundra and tussock tundra.The clearest trend observed was that SOM quality was highest in moist acidic tundra samples, followed by tussock tundra and lowest in non-acidic tundra.Results suggest that generalizations about the SOM quality in soils under different tundra types could be made.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".