Water activity : a fast and non-destructive method to manage orthodox tree seeds moisture prior to storage in regard of seed moisture behaviour variability
Bibliographic record
Abstract
Water activity (aw) is a fast and non destructive method to assess moisture of orthodox tree seeds as well as other hygroscopic matrixes. aw, as equilibrium Relative Humidity (eRH), is a reliable moisture indicator because it is a function of the water potential (Ψ) which depicts the energy status of water and its subsequent chemical and biological availability in hygroscopic matrixes.\nCemagref has demonstrated the efficiency of aw applied to moisture management of major temperate orthodox tree seeds. Since 2007, the Ministère des Ressources naturelles et de la Faune (MRNF) du Québec cooperates in a joint research project with Cemagref; the MRNF has reported the same efficiency of aw with major orthodox boreal species of Québec. The moisture behaviour of the seeds and the determination of specific optimal aw values for storage were investigated in France and Québec by the mean of the construction and interpretation of numerous sorption isotherms. Despite a significant intra and inter specific variability in moisture behaviour, it appears that every optimal storage value determined for each orthodox species is very close to aw 0.35 (140 MPa water potential).\nConsequently, we think that aw 0.35 could be used as a safe moisture target value for the management and storage of diverse orthodox reproductive material in gene banks. This application is particularly appropriate for scarce, high priced seed lots or seeds of minor species for which optimal moisture requirements prior to storage are not available.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".