Bibliographic record
Abstract
The global reliance upon cereal grains, not only for domestic consumption, but also for export in international markets continues to be critical to many countries’ economies. The ecological impacts of the various steps along the supply chain required to get product to the consumer, whether it be fuel, feed, or food, have significant environmental impacts. Ecological assessments have focused historically upon carbon footprints, but by considering other measures of life cycle assessments (LCA), we can come to a better understanding of the environmental significance that some of the most critical crops in our world have. The goal of this study was to compile environmental impact data from published literature and conduct synthesis to determine ecological trends. Published data was compiled and analyzed to determine where critical environmental shortcomings were in the cereal grain industry. Analysis of these data will enable recommendations to be made concerning the weaker spots in supply chains (i.e., more environmentally impactful). In addition, by expanding the geographic locations to an international scale, this study will allow for environmental impacts to be assessed based on various approaches found across the globe. As long as our world continues to place significant emphasis on cereal grains as foundations for societies, we need to better understand the ramifications of these critical crops' ecological impacts and how best to address them.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".