Bibliographic record
Abstract
M.L.E. Reed1,* and Bernard R. Glick2 1 Monsanto Canada Inc., 810-180 Kent Street, Ottawa, Ontario, Canada, K1P 0B6. 2 Department of Biology, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1. * Correspondence author: lucy.reed@monsanto.com The world’s population currently includes around 7 billion people and is expected to increase to approximately 8 billion some time around the year 2020. As a direct consequence of increases in both environmental damage and worldwide population pressure, global food production will need to become more effi cient to feed all of the world’s people. Thus, it is essential that agricultural productivity be signifi cantly increased within the next few decades. Motivated by increasing demand, and by the awareness of the environmental and human health damage that can occur as a consequence of overuse of pesticides and fertilizers, agricultural practice is moving to a more sustainable and environmentally favourable approach. This includes both the increasing use of transgenic plants (e.g., https://www.isaaa.org/ inbrief/default.asp) and plant growth-promoting bacteria (Reed and Glick 2004) as a part of mainstream agricultural practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".