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 Over the last decades, urbanization and its impact on the demand for infrastructure have drawn much attention in global studies and policymaking. Road infrastructure has contributed to a compound annual growth of approximately seven percent. This quick growth of road infrastructure has brought several challenges. Meanwhile, with the availability of high-resolution remote sensing images, extraction of accurate road information has become a fundamental challenge in image processing and geospatial-related technologies. This study proposes a novel framework for road extraction and automated vectorization process. The proposed model excavates contextual information from high-resolution images to restore the topological information of road features. Further, we evaluate our model’s extraction capability on online and acquired datasets. The experimental results demonstrate that the proposed model outperforms the state-of-the-art architectures by obtaining 97.42% and 97.18% overall accuracy and 77.15% and 72.66% IoU for the Ottawa Road Imagery and Indian dataset, respectively. The extracted road features are further mapped, restoring the geospatial information. This analysis would help develop the database for the urban observatories and help sustainably plan future developments.
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 | 0.001 |
| 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