Water activity in seed and pollen banks: an efficient tool to improve conservation of forest genetic resources
Bibliographic record
Abstract
Water activity in seed and pollen banks: an efficient tool to improve conservation of forest genetic resources.\nGenetic diversity of forest reproductive materials, in combination with environmental effects, induces significant intra-specific phenotypic diversity of materials like pollen and seed lots. This makes it difficult to predict the moisture behaviour of forest reproductive material and consequently complicates management procedures.\nWe built and analysed numerous sorption isotherms to describe the moisture behaviour of forest tree seed and pollen lots. As expected, we obtained moisture behaviour models and, more advantageously, optimal equilibrium relative humidity (eRH) values for seed and pollen storage.\nWater activity (aw) measurement is a non destructive, portable and rapid technique to assess moisture content. Both aw and eRH are reliable indicators of the status of water in compounds like seeds or pollen because they are a function of the water potential (Ψ), or 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 water potential and the variable amounts of hygroscopic (starch, proteins) and non hygroscopic (lipids) components. Moreover, sorption isotherms reveal a significant intra-specific variability of resulting MC for a given aw, seriously weakening the operational prediction and use of gravimetric moisture content from eRH.\nWater activity assessment appears to be a reliable moisture monitoring technique for materials having high phenotypic variability; it results, for a given species, in reproducible moisture management procedures based on single recommended aw values. Therefore, aw will be a useful tool for improving the conservation of diversity.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".