Buğday Saplarinin Kompozit Levha Üretiminde Kullanilmasi
Bibliographic record
Abstract
There is a growing pressure on finding alternative fiber resources due to decrease in available forest resources, rising of timber prices, and growing environmental pressure on timber harvesting. This subject has gotten the attention of many scientists in well-developed countries. Specifically, in countries like Canada with its huge forest resources and large volumes of available wood residues for the composite industry, there is an increased interest in the use of agricultural residues for composite panel manufacture. The successful production of wheat straw-based composites has been established after extensive technological developments to overcome the handling and processing problems. Resulting composites are high quality products that can stimulate the consideration and development of other value-added building materials using agricultural residues. Considering the annual production and availability of wheat straw in Turkey, there is a great potential for the production of composite panels as an alternative material to conventional panels such as particleboard and fiberboard In this paper, the utilization of agricultural waste materials as new fiber resources in the manufacture of composites like particleboard and fiberboard and their potential benefits were briefly discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".