Assessment of the frost hardiness of shoots of silver birch (<i>Betula pendula</i>) seedlings with and without controlled exposure to freezing
Bibliographic record
Abstract
Frost hardiness (FH) of the shoots of silver birch (Betula pendula Roth) container seedlings was assessed by electrolyte leakage (EL), visual scoring of damage to the stem (SB), and whole-plant viability tests after controlled exposure to freezing during frost hardening. FH was calculated as the temperature causing a 50% or 10% increase in electrolyte leakage (ELT50, ELT10), stem browning (SBT50, SBT10), proportion of damaged seedlings (DT50, DT10) or mortality (LT50, LT10). Heterogeneous variances in error are considered in the estimation of the above indices by nonlinear regression of the logistic function. A method for computing the standard error of the estimated temperature causing 10% damage is described. The FH estimates by the most reliable method of assessment SBT50were then compared with electrical impedance parameters (EIS) and water contents (WC) measured without controlled exposure to freezing. Comparison of FH assessment methods showed that ELT50estimated the DT10, SBT50, and DT50well. The WC of the uppermost 10 cm of stem decreased in early autumn, when FH was above 10 °C. When the rapid increase in FH started, WC stabilized. Intracellular and extracellular resistance measured by EIS of unexposed stems correlated positively with FH.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".