Nitrogen fertilization for new plantings of hybrid hazelnuts in the Upper Midwest of the United States of America
Bibliographic record
Abstract
Braun, L. C., Gillman, J. H., Hoover, E. E. and Russelle, M. P. 2011. Nitrogen fertilization for new plantings of hybrid hazelnuts in the Upper Midwest of the United States of America. Can. J. Plant Sci. 91: 773–782. Seed-propagated hybrids of Corylus avellana and C. americana are a potential crop for the Upper Midwest. Current N recommendations for hazelnuts are based on research on clonally propagated C. avellana in Oregon and may not be applicable in the Upper Midwest due to differing soils, climate, and plant genetics. We established three field plots in 2003 to test N fertilization rates on new plantings, with rates up to 33 g N plant−1as ammonium nitrate applied annually in the spring, starting 2 wk after transplanting. We observed a strong negative linear effect of N rate on plant survival. In the second year we added trials on same-aged plants that had not previously been fertilized and found no N effect on survival. We concluded that waiting 1 yr after transplanting before fertilizing increases plant survival, but even then N requirements during establishment years are very low for hybrid hazelnuts. Standard leaf N concentrations for C. avellana in Oregon are roughly applicable to hybrid hazelnuts, except that the threshold between deficiency and sufficiency should be raised slightly to 1.9% N. The current threshold between deficient and sufficient, 2.2%, should be regarded as a target, rather than as a threshold to be exceeded.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".