Considerations with selecting turfgrass varieties and cultivars
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
The National Turfgrass Evaluation Program (NTEP) conducts trials of turfgrass species across the USA and Canada. NTEP data collected since 1981 is publicly available at www.ntep.org. Data is collected on more than 100 parameters including key factors in sustainable turfgrass selection. Selection of turfgrasses with traits such as improved disease resistance, drought tolerance, persistence under poor soil conditions or low quality irrigation and fast establishment are shown to protect the environment, improve athlete safety, reduce inputs, thus improving sustainability. To improve access to sustainable turfgrass data, NTEP and the University of Minnesota have developed a relational database along with a user-friendly selection tool, the Turfgrass Trial Explorer. This tool is accessed at www.ntep.org/database.htm, which allows for additional selection, sorting, analyzing and downloading of NTEP data. Case studies are presented to demonstrate current and future use of the Turfgrass Trial Explorer for sustainable turfgrass selection.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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