Yield and composition of lipophylic extracts of yellow birch (Betula alleghaniensis Britton) as a function of wood age and aging under industrial conditions
Bibliographic record
Abstract
Abstract The lipophylic extracts of yellow birch (Betula alleghaniensis) have been investigated to detect the effect of tree age and wood storage time on extract composition. A total of 17 wood disks were cut from trees belonging to different age groups at 1 m above ground and the wood was milled as usual for extraction (laboratory samples). In addition, 49 sawdust samples were collected in a lumber mill to study the effect of industrial processing on the extractives (industrial samples). All laboratory and industrial samples were extracted with dichloromethane under sonication. The chemical composition of the lipophilic extracts obtained was analyzed by GC-MS. A systematic (quasi-linear) relationship was found between the lipophilic extract yield and specimen age. A total of 30 constituents from yellow birch extracts have been identified, 26 of which have never been previously reported for B. alleghaniensis wood.
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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".