Reasons for large annual yield fluctuations in wild arctic bramble (<i>Rubus arcticus</i> subsp. <i>arcticus</i>) in Finland
Bibliographic record
Abstract
Fluctuations in the yield of wild berries are markedly influenced by weather conditions. However, the cause–effect relationship is often poorly understood. Based on data spanning a 20-year period in Finland, we made an effort to elucidate the influence of different weather conditions on the yield of arctic bramble (Rubus arcticus L.). We analyzed the regression coefficients of various weather conditions in several regression models using the elaboration approach. Temperature accumulated in July had a positive effect on yield. Yield was negatively influenced by temperature accumulated during the previous summer, rainfall in the October of the previous year, and temperature accumulated in May of the same year. It is notable that the same weather conditions had a positive influence on yield of the same year whereas these conditions had a negative effect on the yield potential of the following year. Compared with traditional analysis methods, the elaboration approach provided a better understanding of the relationship between weather parameters and yield. The rarity of a good yield could be explained by the particular vulnerability of arctic bramble to the negative effects of weather conditions. Some of these factors could be controlled in field conditions when cultivating arctic bramble.
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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 | 0.001 | 0.001 |
| 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 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".