ENDÃSTRÄ°YEL BOYUTLU SULFURLU CEVHER STOKLARININ Ä°STATÄ°STÄ°KSEL OLARAK MODELLENMESÄ°
Bibliographic record
Abstract
Enterprises producing/consuming high amounts of ore are obliged to work in stocks to be affected by problems arising from production disruptions.Made of industrial-scale due to self-heating may occur in the stock, economic, security operation and environmental problems.In t his study, Canada / Sudbury in producing large quantities of sulphide ore and the production of surplus stock of a company in the field of storing ore inventory was carried out in the field.The company's stock area; respectively at an ore pile of 6.0x9.0x3.0 m width, length and height is formed.Temperature inside the stockpile created by placing the stock in the interior of the 6 temperature sensor data is collected.At the same time the ore pile on the influential parameters such as temperature, air humidity, atmospheric pressure and wind speed is continuously measured values at the same time.The values obtained from the stock multiple regression analysis was applied and we developed a statistical model.Improved correlation coefficients obtained from statistical models have been measured to be approximately 65.3% level.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".