ENDOGENOUS GIBBERELLINS IN DEVELOPING APPLE SEEDS IN RELATION TO ALTERNATE BEARING
Bibliographic record
Abstract
To my family ii ACKNOWLEDGEMENTS I’m grateful to Dr. Peter Hirst for serving as chair of my advisory committee. He persevered beyond my expectations. Without his careful guidance and useful advices, the accomplishments in this thesis would not be possible. I also appreciate the service of Dr. Ronald C. Coolbaugh and Dr. W. Randy Woodson as members of my advisory committee for their advice and encouragement. Thanks for their continuous encouragement and editing during the preparation of this thesis. I gratefully acknowledge the help from professor Richard Pharis and Dr. Ruiquan Zhang in the University of Calgary, Canada. Their expertise and generous help to me on the issue of GA analysis turned out to be one of the most important factors that gave rise to the fruition of this study. I also wish to thank Dr. Mander for the gift of GA standards. I gratefully acknowledge Mr. Kurt Keyes, Dr. Lianming Wu from Chemistry Department for their assistance on my access to the GC-MS machine, and Dr. Leming Qu from the Statistics Department, Purdue University for sharing his knowledge on data collection and analysis. I also acknowledge my friends and family who provided moral support to make it possible for me to pursue this goal. iii
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".