Comment on egusphere-2023-462
Bibliographic record
Abstract
Abstract. An assessment of the soil and bedrock thermal structure of the Sierra de Guadarrama, in Central Spain, is provided using subsurface and ground surface temperature data coming from four deep (20 m) monitoring profiles belonging to the Guadarrama Monitoring Network (GuMNet), and two shallow (1 m) from the Spanish Meteorology Service (AEMET), covering the time span of 2015–2021 and 1989–2018, respectively. An evaluation of air and ground surface temperature coupling shows soil insulation due to snow cover is the main source of seasonal decoupling, being especially relevant in winter at high altitude sites. Temperature propagation in the subsurface is characterized by assuming a heat conductive regime, by considering apparent thermal diffusivity values derived from the amplitude attenuation and phase shift of the annual cycle with depth. For the deep profiles, the apparent thermal diffusivity ranges from 1 to 1.3 10−6 m2s−1, consistent with values for gneiss and granite, the major bedrock components in the Sierra de Guadarrama. However, thermal diffusivity is lower and more heterogeneous in the soil layers close to the surface (0.4–0.8 10−6 m2s−1). An increase of diffusivity with depth is observed, being generally larger in the soil-bedrock transition, at 4–8 m depth. A new method based on the spectral attenuation of temperature harmonics allows for analyzing thermal diffusivity from high-frequency changes in the soil near the surface at short timescales. The results are relevant for the understanding of soil thermodynamics in relation to other soil properties and suggest that changes in heat diffusivity are related to soil moisture content changes, which makes this method a potential tool in soil drought and water resource availability reconstruction from soil temperature data.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.406 | 0.314 |
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".