Assessing the impact of quartz content on the prediction of soil thermal conductivity
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Bibliographic record
Abstract
Accurate predictions of soil thermal conductivity k are strongly influenced by the volumetric fraction of quartz, Θ q , data for which are very scarce. This paper reveals a new approach to estimate Θ q from measured k records. First, an equation that relates the normalised k of soil (K e ) to the degree of saturation S r is fitted to experimental k data, and the k at full saturation is assessed; then Θ q 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 Θ q 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 K e (S r ) 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 K e (S r ) = 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it