Study on optimal temperature furnace curve based on wavelet transform algorithm
Bibliographic record
Abstract
Based on the equation of furnace temperature curve, the objective function is established by integral, and then the constraint condition is established according to the process boundary. Wavelet transform algorithm to finally, finally the optimum furnace temperature curve, can draw 185 DHS C (small temperature range 1 ~ 5), 208 DHS C (small temperature zone 6), 240 DHS C (temperature range of small 7), 252 DHS C (temperature range of small 8 ~ 9), the corresponding area of 968.24 cm2, again USES the wavelet transform algorithm, and the function such as secondary derivative method, first to second derivative of furnace temperature curve function, make the secondary derived function is obtained through origin of coordinates. Then the constraint conditions and objective function were established. Finally, when the optimal furnace temperature curve was reached, 183°C (small temperature range 1~5), 205°C (small temperature range 6), 241°C (small temperature range 7) and 253°C (small temperature range 8~9), the corresponding area was 1096.38cm2.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".