Advancing geologic document digitalization and information retrieval with generative AI
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
Abstract This paper demonstrates how generative artificial intelligence (AI) enhances geoscientific document processing by improving text analysis, table extraction, and figure classification. Traditional workflows struggle with domain-specific terminology, poor-quality inputs, and rare formats. To address these challenges, we employ domain fine-tuned bidirectional encoder representations from transformers (BERT) models to enhance text processing. Additionally, we utilize multimodal large language models for precise table recognition and context-aware image classification. Finally, a domain-optimized retrieval system, GeoRAG, improves the relevance and accuracy of information retrieval. These AI-driven advancements streamline digitalization, enhance data extraction, and enable efficient handling of complex geoscientific documents. While challenges such as hallucinations, interpretability, and output consistency remain, this study highlights the transformative potential of generative AI for geoscience workflows and decision-making processes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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