Improvement in Durability of Oil Heat Treated 16-Year-Old Acacia mangium in Laboratory Tests
Bibliographic record
Abstract
Improvement in the durability of oil heat treated Acacia mangium through accelerated laboratory tests was studied. A. mangium logs of 16-year-old harvested and segregated into the bottom, middle, and top portions. These were oil-heat treated in a stainless steel tank with oil palm oil as a heating medium at temperatures 180, 200 and 220 °C for the duration of 30, 60 and 90 minutes respectively. The wood samples dried and grounded into sawdust, air-dried again before undergoing durability tests. Accelerated 12 weeks laboratory durability studies conducted on the treated A. mangium. Fungi of Pycnoporus sanguineus, Gloeophyllum trabeum and Coriolus versicolors inoculated on the woods. Untreated samples used as controls. The results showed that durability of the wood improved with an increase in temperature and duration of the treatment. The oil heat treatment process reduced the attack of G. trabeum from 5.02%, 4.41% and 4.38% in the control samples to 0.54-4.55%, 0.91-4.41% and 1.08-4.38% at the bottom, middle and top portions, respectively. The attack of C. versicolors reduced from 11.48%, 14.27% and 15.68% in the control samples to 1.87-10.19%, 3.10-12.69 and 4.78-15.10% at the bottom, middle and top portions. However, the attacked of P. sanguineus were less effective with 31.42%, 18.24% and 10.53% in control samples to 3.71-10.18%, 5.74-14.59% and 4.37-17.08% at the bottom, middle and top portions. Heavy colonization of mycelia occurs in vessels of the untreated A. mangium wood in comparison to the oil heat treated wood observed through scanning electron microscope.
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) | 0.000 | 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 | 0.000 |
| 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".