Effect of Non-Rubber Constituents on Guayule and Hevea Rubber Intrinsic Properties
Bibliographic record
Abstract
To meet the increasing demand for natural rubber (NR), currently sourced from the tropical rubber tree Hevea brasiliensis, and address price volatility and steadily increasing labor costs, alternate rubber-producing species are in commercial development. One of these, guayule (Parthenium argentatum), has emerged on the market as a commercial source of high quality rubber. Non-rubber constituents play an important role in the physical properties of NR products. The intrinsic composition of the two NR materials differs and these differences may be a principal cause of the performance differences between them. We have compared the effect of non-rubber constituents, such as protein, lipids, resin and rubber particle membranes. Firstly, a film casting method was developed to obtain rubber films with a uniform thickness. Secondly, the glass transition temperature of different rubbers was determined by dynamic mechanical analysis, and tensile properties were tested for uncompounded materials. Guayule natural rubber (GNR), from which most of the membranes were removed while in latex form (MRGNR) was found to have higher intrinsic strength than GNR or gel-free NR (FNR). An acetone extraction was performed to quantify the resin and free lipids in the rubber samples.
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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 | 0.000 | 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 | 0.001 |
| 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".