Genetic and environmental variation in alfalfa forage yield from variety testing experiments conducted in North America between 1986 to 1999 (Version 2)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>The yield dataset contains forage yield data from over 700 alfalfa variety tests conducted by researchers in the US and Canada from 1986 through 1999. Some trials also measured forage quality and stand; these data are also available in PURR. The data were aggregated from the Alfalfa Variety Performance Database originally compiled in 2000 by Daniel W. Wiersma and Wayne G. Hartman (doi: <a href="https://doi.org/10.4231/PHKH-4334" target="_blank">10.4231/PHKH-4334)</a>. The database was used to analyze long-term trends in genetic improvement of alfalfa yield and agronomic performance across a broad range of environments.</p> <p>The yield file contains cultivar-specific yield data for each harvest within a year. It is organized by Trial, and includes trial-specific spatial (state, latitude, longitude, elevation), and temporal (year of trial, harvest number, harvest date, planting date) details. Finally, when available soil type and statistics are also provided. A companion dataset in PURR contains the characteristics of these alfalfa varieties (doi: <a href="https://doi.org/10.4231/FMY9-6966" target="_blank">10.4231/FMY9-6966)</a>.&nbsp;</p>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it