Evaluation of soft computing algorithms for estimation of spatial transmissivity
Bibliographic record
Abstract
This paper explored the potential of inverse technique using adaptive network-based fuzzy inference system (ANFIS), self-organised maps (SOMs) and M5P model tree-based regression approach to estimate the spatial transmissivity of aquifer domain. The study is based on coupling of finite element method (FEM)-soft computing (ANFIS, SOMs, M5P) model, which serve as forward (FEM) and inverse (ANN, SOMs, M5P) models. The root mean square error, coefficient of correlation and Nash-Sutcliffe efficiency index are used as comparison criteria for evaluating the models. The results from this study suggest that M5P model tree-based modelling approach is superior in accuracy in comparison to the ANFIS and SOMs model investigated in this study. This study also suggests that M5P model trees, being analogous to piecewise linear functions, have advantages over other techniques as they offer more insight into the developed model and are very efficient in training, and always converge.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".