A carbon-based method for estimating the wetness of forest surface soil horizons
Bibliographic record
Abstract
The degree of wetness in forest surface soils has an effect on chemical and biological processes but is not easily measured. The high spatial variability in carbon (C) concentration creates high variability in water-holding capacity, and gravimetric water content is not informative. Local hydrology can create patchiness in soil moisture, with saturated soils often found near well-drained ones. When sampling to measure such factors as nitrification potential, it would be advantageous to have a simple metric that reflects the relative wetness of the soil. The relationship between C concentration (range 51.5–520.8 g·kg–1) and gravimetric water content was found to be linear for a set of 113 H- and A-horizon samples assumed to be at field capacity. The wetness ratio is defined as the actual water content of a sample divided by the water content predicted by the least squares regression equation based on C concentration (soil water content (kg·kg–1) = 0.080 + 0.0057 soil C concentration (g·kg–1)). Soil moisture retention curves were developed for a small number of samples in the range of 0 to about –10 kPa and showed that the equation predicted that water would be held at relatively high potential. In samples taken from 10 watersheds in the northeastern USA, wetness ratios between 1.25 and 3.1 were associated with soils identified in the field as ranging from wet to boglike. A median ratio of 0.49 was found in a watershed sampled after an extended dry period. At the Sleepers River Research Watershed, high wetness ratios were associated with a high soil calcium concentration, presumably from enriched groundwater. The ratio should be a useful measurement in watershed studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".