ENGREF-FCAP “Flying Wood’’ Method to Characterize Unknown Central Africa Tropical Woods Relative of Their Drying
Bibliographic record
Abstract
When we want to opposite excessive exploitation of some species of wood in the tropics, it is important to characterize unknown woods with intention of their valorization. This is most urgent than in the majority of these countries, only some species are highly exploited and the species less valorized are destroyed during the researches of known species in the forests. ENGREF-FCAP ‘’flying wood’’ method is an alternative to classify unknown woods according to their drying. This method permits us to evaluate deformations of wood sample during a dissymmetrical drying and to estimate also evolution of their drying kinetic. The radius of the curvature of samples and drying kinetic permit us to compare the attacks of the drying air conditions on the samples. When these attacks are capable to destroy the quality of wood, a review of these conditions permits to want favorable conditions until we obtained the drying table of unknown woods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".