Editorial: Application of artificial intelligence in environmental, agriculture and earth sciences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it