Multi‐year simulation and model calibration of soil moisture and temperature profiles in till soil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary In Nordic regions water infiltration into soil is controlled by soil moisture content and frozen soil conditions, which are regulated by soil temperature. For long‐term model predictions of the effects of climate change, models need to be tested with long‐term data to assess model sensitivity to parameter uncertainties under both typical and exceptional conditions. Ten‐year (2002–2011) daily soil moisture and temperature data at different depths in glacial till soils in central Finland were used to assess the sensitivity of a coupled heat and water transfer model, COUP, to model parameters. The model was most sensitive to the parameters controlling snow accumulation and melt, the thermal conductivity of frozen soil and soil water retention characteristics. Observed time series for soil temperature and moisture at different depths were matched reasonably well by model simulations, although the model performance with respect to moisture dynamics in the topsoil was relatively poor. The model was not able to simulate accurately exceptional winter conditions, such as mid‐winter snowmelt events. This study showed that the main characteristics of long‐term variation in soil temperature for till‐derived soil in a cold climate can be resolved by a coupled water and heat transport model. Better characterization of infiltration in cold climates would require measurement of water fluxes, and soil frost occurrence and penetration. Highlights Ten‐year soil temperature and moisture observations are predicted with coupled heat and water model. Snow processes and soil thermal and water retention properties proved critical in our simulations. Exceptional winter conditions pose a challenge in parameterization of the model. Studies measuring water fluxes and soil frost occurrence are needed for advances in modelling.
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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 | 0.001 | 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 | 0.001 |
| 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