Prediction of Oil Production with: Data Mining, Neuro-Fuzzy and Linear Regression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
According to importance and usage of researches of petroleum production, our major goal in this paperis to forecast the oil production by using Data Mining Technique for improving estimation of oil consumption base of History of data. The implement of auto regression, Data cleaning and concept of pre-processing to viewpoint of Time series analysis are traditional concept in intelligent format. We use data cleaning for integration data and auto regression to determine input of model and for pre-processing for upgrade operation of ANFIS. ANFIS algorithm is developed by different data pre-processing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. However, it is concluded that ANFIS provides better results than AR in Canada. This is unlike previous expectations that ANFIS always provides better estimation than conventional approaches.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it