Characterization of the Pyrolytic Products of Pine Nut Shells
Bibliographic record
Abstract
Have been determined the technical characteristics and elemental composition of shells. The elemental composition of the shell was determined by a microanalytical method such as 5Е С2000 model CNH-analyzer. The pyrolysis of shells investigated by using a standard quartz retort (tube) at different heating temperatures and determined the yields of pyrolysis products such as hard residue, tar, pyrolytic water, and gas. As a result of these experiments have been determined that 30% hard residue, higher yield 13% of tar, can be obtained at heating temperature 500oC. Thermogravimetric analysis of shells carried out in TG/DTA7200, Hitachi, Japan model equipment. The shells’ ash chemical composition was first time determined by the X-ray diffractions powder, that it consists of significantly higher 40% these chemical elements including manganese, nickel, little zinc, sulfur, aluminum, phosphorus, iron, magnesium, and calcium. The solubility of purified pyrolysis tar of shells in hexane, benzene and dichloromethane were investigated by using silicagel column and the chemical composition of each fraction determined by using of GC/MS chromatography system. The FTIR spectra of shell and pyrolysis tar determined by using of a Nicolet 20-PC spectrometer. The porosity structure of activated pyrolysis hard residue determined by the SEM analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".