Soil mineral structural water loss during loss on ignition analyses
Bibliographic record
Abstract
Water loss from soil minerals has been known to cause errors in the determination of soil organic matter when the loss on ignition (LOI) method is used. Unfortunately, no known published studies reliably quantify the range of structural water in the soil. To do this, 15 common reference minerals were analyzed by LOI to obtain their individual water loss. In addition, 14 upland, loamy soil samples and 3 wetland/hydric soil samples with varied mineral contents were analyzed to collect their X-ray powder diffraction spectra. Based upon X-ray spectra peak intensities, the modal abundance of minerals in each soil sample was determined using the RockJock computer program. The resultant modal weight percentages of all identified minerals in each soil sample were then multiplied by the LOI value for each mineral to obtain the mineral structural water loss (SWL) of that soil sample. For the 17 soil samples analyzed, the range of mineral water loss is 0.56 to 2.45%. Depending on the LOI values of the soil samples, the SWL:LOI ratios range from 0.04 to around 1.00. The SWL:LOI ratios are particularly low for top wetland soil when the LOI value is higher. The ratios are lower for surface soil samples than for subsurface soil samples because of the high LOI values in surface soil samples. Understanding soil mineral water loss and its relation to the LOI patterns from various environments is important for the accurate evaluation of soil organic matter when the LOI method is used. Key words: Mineral, structural water, loss on ignition
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".