Comparison of the Performance of Statistical Models that Predict Soil Respiration from Forests
Bibliographic record
Abstract
Soil respiration (Rs) is an important source of CO 2 to the atmosphere, yet understanding the processes controlling the combined autotrophic and heterotrophic components has proven challenging. Numerous statistical models have been developed to explain Rs as a function of physical and chemical conditions, but there has been little effort to systematically evaluate and compare the performance of these different models. Soils in a sugar maple ( Acer saccharum Marsh.) forest were monitored for Rs and its physical and chemical drivers, and the monitored data were used to fit the most common of these statistical models. Each model was fit using a repeated trials approach and the performance of the model was evaluated with a range of goodness‐of‐fit measures (RMSE, mean absolute error, r 2 , index of agreement, bias, and Akaike information criterion). An exponential model in which the exponent is a polynomial expression that is linear with respect to temperature (°C) and quadratic with respect to soil moisture (% by volume) explained 57% of the variance in Rs and performed well among the goodness‐of‐fit measures. Inclusion of C quantity and substrate quality (as measured by the C/N ratio) in the soils, however, increased the explanation of variance in Rs to 71%. This study shows that combining soil temperature and moisture drivers with soil C quantity and substrate quality significantly improves statistical models predicting soil respiration. Our findings support the comparison of the performance of different statistical models using a repeated trials approach coupled with a range of goodness‐of‐fit measures to identify controls on Rs within an ecosystem and to assess the generality of these controls on Rs across ecosystems.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".