Measurement of water activity on forest tree seeds: an efficient tool for seed bank management
Bibliographic record
Abstract
Water activity (aw) measurement is a non destructive, portable and rapid technique to assess moisture of hygroscopic materials. aw is very close to equilibrium relative humidity (eRH). Both are reliable indicators of the status of water in compounds like seeds because they are a function of the water potential (Ψ) which is the energy status of water in hygroscopic matrixes. Gravimetric moisture content (MC) of a given sample is not a factor but a consequence of the combination of a given water potential and an unpredictable ratio of hygroscopic (starch, proteins..) and non hygroscopic (lipids) components.\nWater activity is used in routine by many seeds banks like the Millenium Seed Bank project managed by the Kew Royal Botanic gardens. aw is considered in this application as a non destructive assessment of seed moisture which can be turn into moisture content by reference to the sorption isotherm data. \nHowever, Cemagref demonstrated that sorption isotherms revealed, for forest materials, a significant intra-specific variability of resulting MC for a given aw or eRH, this finding seriously weakens the operational prediction and use of gravimetric moisture content from eRH or aw. This has been confirmed by the work conducted at the Direction de la Recherche Forestière (Québec) on boreal species.\nAs a result, aw appears to be a particularly suitable moisture management technique for seed produced by species with high level of diversity like forest reproductive materials. Consequently, we consider aw as a valuable reference to qualify moisture status of seeds and resulting predictable stability versus chemical or biological hazards. Therefore, aw can be used as a very valuable tool for seed lot moisture management from collection to storage.
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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 | 0.001 |
| 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 | 0.001 |
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".