Comparison of different methods for sensitivity analysis of composting modelling
Bibliographic record
Abstract
Sensitivity analysis was used to optimize a numerical model of the composting process. Sensitivity analysis of composting models usually considers perturbation as a fixed decrease or increase of several individual parameters. In this study, the most sensitive combination of perturbations was found. Since compost is a complex system, interactions involving microbial growth parameters were the primary focus, such as maximum microbial growth rate or compost heat capacity, rather than the isolated effects of each perturbation. Instead of examining two discrete, lower and higher, values for each parameter, a continuum of perturbations was assumed. The study focused on small perturbations, limiting the sum of the squares of the perturbations to a chosen value. The perturbations on parameters were chosen in proportion to their initial values. Several methods were reviewed to find the maximum error in the hypersphere of perturbation. Available time and resources limited the amount of simulation, so a comprehensive experimental grid design was not realistic. Several optimization algorithms were explored in order to limit the amount of simulation required. The chosen error function had specific characteristics that allowed the use of specialized methods in this context. The maximum error was approximated through the first order partial derivatives of the function. An evaluation of the amount of simulation required was done to compare it to some common algorithm methods, and those methods are summarized and compared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".