Re-tuning the Walker-Kasting global carbon cycle box model using a parameter sensitivity analysis
Bibliographic record
Abstract
introduction: The Walker-Kasting global carbon cycle box model is a simple representation of the earth system used to study climatic events. This model has a high number of parameters whose sensitivity must be tested in order to better understand which of them dominate the behaviour of the model. In this study, we perform a parameter sensitivity analysis. moreover, we use these results to re-tune the model to preindustrial conditions using a quantitative criterion. We then compare our results to those determined by Walker-Kasting. Methods: We achieved the parameter sensitivity analysis by calculating, for each parameter, an index that measures the impact of a change in the initial parameter value on the equilibrium solutions. The most sensitive parameters were determined and then tuned in the model by comparing the model equilibrium solutions to a set of 32 experimental values. results: We found that nine of the tuning parameters were sensitive to a change to their initial value. Furthermore, we discovered that 5 of these parameter values were identical to those determined by Walker-Kasting, thus affirming their work. discussion: a sensitivity analysis is interesting to perform because it allows the users of a model to more fully comprehend the way in which the model reacts to changes in its parameters. sensitivity analysis is fundamental in the tuning of a model (for example, to a particular period in the earth’s history) since it allows researchers to consider only the most important parameters.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".