Bibliographic record
Abstract
The Population of the world is now eight billion people, but it is expected to reach 9.7 billion by 2050 due to ongoing population growth[1]. A population of this size confronts several challenges, the most significant being the requirement for expanded global food production to meet nutritional demands. Due to the inefficient use of natural resources that overburdens the ecosystem, traditional agricultural practices are thought to be unable to meet these demands[2]. We are, thus, forced to adopt more ecologically friendly agricultural methods. By lowering the need for external inputs and assisting farmers in coping with unfavorable weather, which is becoming more frequent due to climate change, smart farming technologies (SFTs) can increase crop output in a sustainable manner, enhancing its resilience and lowering carbon dioxide emissions. The United States had a$91 \%$acceptance rate for SFT as of 2020[3], with Canada, Australia, and other European nations lagging behind as the most adopting nations. A wider use of SFT is needed to achieve the objective of feeding the world’s population sustainably, even if certain emerging nations are accelerating their adoption rates.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".