Assessing the impact of quartz content on the prediction of soil thermal conductivity
Bibliographic record
Abstract
Accurate predictions of soil thermal conductivity k are strongly influenced by the volumetric fraction of quartz, Θq, data for which are very scarce. This paper reveals a new approach to estimate Θq from measured k records. First, an equation that relates the normalised k of soil (Ke) to the degree of saturation Sr is fitted to experimental k data, and the k at full saturation is assessed; then Θq is calculated from a geometric mean model. This modelling approach was applied to the k data of 10 Chinese soils obtained by Lu et al., also containing k measurements at full dryness, and to soils investigated by Kersten with measured quartz content data. The fitted Θq data for Chinese soils are noticeably different from the sand mass fraction, commonly assumed in the past as an equivalent of quartz content, consequently leading to irrational k estimates. Acceptably good agreement was obtained between fitted and measured quartz content for Kersten's soils. Five Ke(Sr) functions were tested against the experimental data for ten Chinese soils, supplemented with calculated k at full saturation. Overall, the normalised function by Lu et al. was the most suitable for the soils tested. The assumption that Ke(Sr) = 0, applied to Johansen's model extended to full dryness, worked well for fine soils, and was acceptable for coarse soils.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".