Editorial: Application of artificial intelligence in environmental, agriculture and earth sciences
Bibliographic record
Abstract
Integrating artificial intelligence (AI) into environmental, agricultural, and earth sciences heralds a new era of innovation. This Research Topic unveils the transformative role of AI in addressing some of the most pressing challenges in these domains. Some skeptics argue that AI's role in these fields is overrated, potentially leading to an overdependence on technology and the overshadowing of traditional methods. Concerns about losing human insight and ethical considerations in data handling are also raised.While acknowledging the importance of traditional methods, the complexity of today's environmental and agricultural challenges necessitates advanced solutions. AI enhances, rather than replaces, human expertise. Critics often overlook the synergy between AI and human skills, which is crucial for innovative problem-solving. In conclusion, AI in environmental, agriculture, and earth sciences is not merely a technological leap; it's an essential step towards a sustainable future. These studies demonstrate AI's capacity to work alongside human expertise, offering innovative solutions to complex challenges. As we navigate the intricacies of our planet's needs, AI emerges not as a competitor but as a crucial ally in our journey toward sustainability and ecological balance.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.016 | 0.018 |
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".