Water activity: A new tool for moisture management of seedlots in tree seed centres
Bibliographic record
Abstract
Measurement of water activity (Aw) is a concept developed, and mainly used, by food-processing and pharmaceutical industries. Contrary to gravimetric moisture (MC) content that quantifies the total amount of water in a product, Aw qualifies the intensity of the connections between water and other molecules (such as lipids, carbohydrates or proteins) and, therefore illustrates water availability and mobility in the substance. Aw has three main operational advantages: rapid measurement (less than 20 minutes per sample); non-destructive testing, making it very interesting for use with rare or valuable samples such as pollen or seeds from specific crossings or ex-situ genetic resource conservation; ease of use, requiring little training.\nIn 2007, the DRF (MRNF, Québec) decided to adopt the method developed by Cemagref (France). The work began with black spruce (Picea mariana) and jack pine (Pinus banksiana), the two most important species in the Québec reforestation program. To date, our results follow the same pattern as those obtained in France. An optimal Aw is determined for each species which will be used to optimize seed processing in tree seed centres. Depending on the seedlot, a given Aw may result in different MC because of the variability in seedlot traits like maturity, origin or crop year. Managing seedlots with Aw makes moisture management non sensitive to genetic, and consequently phenotypic, diversity which is of a great interest for conservation purposes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".