Debut of a flexible model for simulating soil respiration–soil temperature relationship: Gamma model
Bibliographic record
Abstract
A number of empirical models are used in literature to simulate the response of soil respiration (Rs) to soil temperature (Ts). The most widely used ones are the exponential Q 10 model and the sigmoid‐shaped Lloyd‐Taylor and logistic models. None of these models are applicable across a wide range of ecosystems or climates, and none allow Rs to decrease at high Ts values. Here we present a new, more flexible, empirical model, the so‐called Gamma model, which can take on the shapes of the three models mentioned above and is mathematically flexible enough to allow for Rs to decrease at high Ts values, as dictated by data. We compared the Gamma model fits to the Q 10 , Lloyd‐Taylor, and logistic models, using coefficient of determination (R 2 ), residual sum of squares, and Akaike's Information Criterion. The models were tested across a wide Ts range (−18 to 35°C), in five forest ecosystems, spanning three different climate zones: boreal, temperate, and Mediterranean. Compared to the other three models, the Gamma model performed either better or as good as the other models in simulating the Rs‐Ts relationship at all sites. Simulations were carried out using models parameterized by the ordinary least squares and weighted absolute deviation estimation methods. Rs values derived from the two estimation methods were comparable once the proper functional form for the Rs‐Ts model was chosen. We also show how the Gamma model can be expanded, using simple mathematics to help researchers analyze the Rs‐Ts relationship in the context of other environmental factors, such as soil moisture and nutrients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".