Drying schedule structure and subsequent post-drying equalisation effect on hemlock timber quality
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Western hemlock (Tsuga heterophylla), was dried using two different schedules with optional conditioning and seven day post-drying equalisation in a covered and climate controlled space that emulates outdoor conditions in south-central Japan from October to May. Timber quality was evaluated pre- and post-drying, and/or post-equilibration. Drying times, moisture content variation between and within timbers and internal stresses were also assessed. Data analysis revealed that conditioning and aggressive drying reduced casehardening, while milder drying resulted in less twist and diamonding. Although the control run seemed to develop less shape distortions than the treatments, it required longer drying times. With aggressive drying the kiln turnover will be quicker and the dried timber might be stored in a covered area for post-drying equalisation that will level out moisture gradients and alleviate casehardening. As a subsequent step, the timber might be planed to reduce twist, diamonding and superficial checks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it