American Forage and Grassland Council Technology Interaction and Policy Development
Bibliographic record
Abstract
The American Forage and Grassland Council (AFGC) is a national organisation which has been in existence since 1968. Membership of AFGC is about 2,500. The membership of AFGC is divided into three main sectors: private, public and industry. The private sector has the largest membership (60%), and private members are usually producers that are engaged in some type of agricultural enterprise involving the use of forages. The public sector members (30%) are educators and other government agency personnel that work with the general public. The industry sector (10%) involves various companies that deal with the forage industry. The AFGC Board of Directors is composed of 18 members, 6 from each sector. Most of the AFGC membership belongs to an affiliate council. There are currently 25 affiliate councils in the United States, most of which are located in the eastern, southern and midwestern regions of the country. There is one affiliate council located in Canada (Ontario). One of the major strengths of AFGC lies in its diversity of membership among the three sectors. The primary core purpose of AFGC is to advance forage agriculture and grassland stewardship. This organisation has the vision to be recognised as the leader and voice of economically and environmentally sound forage agriculture.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.005 |
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".