Morphological Characterization of Chicken Feather Rachis, Neem Sawdust, and High Density Polyethylene (HDPE) Reinforced Composite Material
Bibliographic record
Abstract
In this paper research was expanded on waste that can be recycled. There are so many different types of waste products available in our country that would ruin the environment. Three different products are considered to reduce some of the problems among them two are fibers and the other is polyethylene. Chicken feather rachis and Neem sawdust are the two different fibers and the reinforced material is high density polyethylene (HDPE). These materials are made as seven different compositions to find the ability of the sample in different characterizations. The seven compositions are 80H-10CF-10SD, 80H-5CF-15SD, 80H-15CF-5SD, 80H-20CF, 70H-30CF, 70H-30SD, 80H-20SD, and these compositions are made in CIPET, Hyderabad by using single screw extruder& strand pelletizer. These granules are prepared with the help of Injection moulding, the samples are examined by various testing to prove the ability, strength, structure of materials in individual, and combination forms. The testing conducted for the samples is that the Morphology of the sample was characterized using FESEM, XRD. It is examined that the ratios are light weight with high strength by using the testings and it gives perfect images without any cracks on the surface and no more defects among these ratios.
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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".