Alfalfa Variety Trial Database, 1986-1999
Bibliographic record
Abstract
The dataset contain archived copy of the Alfalfa Variety Performance Database originally compiled in 2000 by Daniel W. Wiersma and Wayne G. Hartman that contains forage yield and quality results, and plant stand estimates from over 700 alfalfa variety tests conducted by researchers in the US and Canada from 1986 through 1999. The database was used to analyze long-term trends in genetic improvement of alfalfa yield and agronomic performance. Data were originally compiled in an Access database. This publication contains the database both in the Access database format (*.mdb) and as mysql dump (*mysql). The *.mdb file was fully operational in the version Access 2019 and the mysql data were successfully inserted into a MySQL data prior to their archiving. In addition, the data in individual tables were exported as a set of twenty one CSV files (plus two edited files) that were also preserved in this dataset. Four additional datasets were created from the database tables and published in PURR. The alfalfa variety metadata dataset contains descriptors and characteristic of over 2900 alfalfa varieties and experimental lines used in these variety trials. Three companion datasets extracted from the original database contain the yield, forage quality, and stand persistence data.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.021 |
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".