Proceedings. Ohio Grape-Wine Short Course, 1982
Bibliographic record
Abstract
Welcome and comments / Roy M. Kottman -- Surveying your vineyard for winter injury / G. R. Nonnecke and G. A. Cahoon -- Grape juice, a new opportunity for enterprising growers / A. M. Adams -- Bitter rot of grape, a potential problem in Ohio ; Fungicides for disease control in grapes / Michael A. Ellis -- The use of copper and lime on grapes / Thomas J. Zabadal and Thomas J. Burr -- Developing integrated pest management for grapes in Pennsylvania / Gerald L. Jubb, Jr. -- Use of carbonates in wine deacidification / Leonard R. Mattick -- Induction of malolactic fermentation in Ohio red table wines / Jim-Wen Liu and James F. Gallander -- Experiences and research with wine grapes in Pennsylvania / Carl W. Haeseler, George M. Greene, III and John O. Yocum -- Principles of winery sanitation / R. F. Belscher -- Evolution of commercial quality standards for Ontario wines / A. M. Adams -- Computerizing a small winery / Norman E. Greene -- Spray adjuvants are management tools / T. E. Whitmore -- Acid and pH control in finished wines / Leonard R. Mattick -- Cluster and shoot thinning as a commercial cultural practice / G. A. Cahoon and G. R. Nonnecke -- A new look at some old grape pests / Gerald L. Jubb, Jr. and Andrew J. Muza -- Considerations for making high quality vidal blanc wines / James F. Gallander and Judy F. Stetson -- Environment, safety and pesticide labels / William F. Lyon
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.790 | 0.619 |
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".