Bibliographic record
Abstract
Organization of scanner data for more effective usage in managerial decision-making, by Oral Capps, Jeffrey M. Thomas, and Don L. Long; Effects of entry and changes in demand on concentration in wholesale grocery markets, by Walter B. Epps and Charles Handy; U.S. fruit and vegetable processing industries: a structural perspective, by Katherine C. Buckley, Shannon R. Hamm, and Ben Huang; Price formation over space in the U.S. sweetener market, by Dr. Dagher and A. Debatisse; Preferred points of origin for selected foods, by Robert L. Degner and J. David Locascio; Retail produce managers evaluate sweet potato shipping containers, by Dr. Edmund A. Estes; Impact of shelf space characteristics on consumers' produce buying decisions, by John J. VanSickle and German Molina; Consumer acceptance of convenience-oriented meat products: pre-cooked vacuum packaged beef roasts, by Floyd K. McKeith, Peter J. Bechtel, and Michael A. Hudson; The relationship between store image and shopping behavior for different consumer groups, by Dr. Robert S. Welsh; One way to gather information on supermarket customer appeals, by James J. Corbett and John J. McGill, Jr.; Hedonic price analysis of fresh tree fruits at the retail and producer level: an investigation of nutrient values and experience characteristics, by Mark L. Waller and Sarahelen Thompson
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.630 | 0.452 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".