An application of artificial neural network to predict the added value of oil, gas and petroleum industry products
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
This paper presents an empirical investigation on fluctuation and trend of added value changes in oil and gas industries and their products and also to anticipate the current added value of these industries. For this aim, fluctuation of added values of different subsidiaries such as oil group products, exporting crude oil, production of gas and petroleum is investigated over the period 1959-2004. The study selects the best network to anticipate added value in subsidiaries of energy section. For the training and testing the network, all data are divided into two groups. To define input layer neurons number which are equal to auto regressive vector rank in ARMA method, the rank of auto regressive (p) and mobile mean (q) have been used according to proposed method of Pesaran & Pesaran. The simulated results have been extracted by using neural networks, in feed forward network which had low compatibility with the real added value.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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