Bibliographic record
Abstract
Agricultural conversion is a major reason for deforestation that affects the United States and is responsible for the loss of species, soil depletion and global warming. This work aims to analyze the use of AI for combating deforestation in the agricultural sector in the United States through improved surveillance, risk assessments, and policy modeling. This proposed framework combines satellite imagery data, agricultural records, and selected socio-economic factors and uses CNNs, GBMs, and ABMs to tackle deforestation systematically. CNN also showed an accuracy of 94% in the identification of the area of deforestation, while the GBMs showed an accuracy of 0.92 AUC-ROC in identifying hotspot areas. Through ABMs that assumed policy changes such as reforestation incentives and fines for violators, the study showed that deforestation rates could be cut by up to 25%. Regression and correlation analyses and hypothesis testing proved significant predictors such as crop yield, rainfall variability and the superiority of the models to conventional techniques. The outcomes reveal that AI can offer an effective solution to increase food production and maintain forests at the same time. This framework allows for the formulation of specific recommendations for policy initiatives because it incorporates empirical evidence. Further research should improve the modularity, the real-time monitoring system and the access to the algorithm to further increase the impact of AI on sustainable land management and the chopping down of forests.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".