Re-analyzing historical records of winter injury in Finnish apple orchards
Bibliographic record
Abstract
Frequent winter injury is a major problem in apple (Malus domestica Borkh.) production in northern areas. Discriminant and cluster analysis were used to determine the critical climatic factors associated with winter injury occurrences in south-western Finland over a 71-yr period, 1927 to 1998. Years with and without winter injury were best characterized by variables indicating mid-winter severity: the monthly mean, minimum, and maximum temperatures from January to March. Also, weather conditions during the preceding summer and fall contributed significantly to the discrimination between winter injury and no injury years. A decrease in growing degree days, drought in August, and a high level of precipitation in September were associated with winter-kill years, probably due to their impact on the annual cycle of vegetative growth. The hypothesis on the adverse effect of mild spells during early and mid-winter could not be verified by this study. Mid-winter frost resistance was confirmed as the most important winter hardiness characteristic in the semi-maritime climate of Finland. At high latitudes rootstocks and cultural practices should be chosen to ensure that trees acquire vegetative maturity even under adverse weather conditions. Key words: Malus domestica Borkh., winter injury, climate, variety, discriminant analysis, medoid clustering
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".