Bibliographic record
Abstract
Due to increasing number of High Resolution Satellites, the production of ortho image and 3D maps from these images are becoming increasingly important.In order to do so acquiring Ground Control Points (GCP) is necessary.Higher quality aerial or satellite imagery will not replace the need for GCP.In fact, ground control point collection becomes increasingly more important as image quality improves.Fortunately, collecting ground control point is now a much faster, more accurate, and cost-effective process thanks to the use of GPS but the proper number of control points still is an issue.In this article, the P5 imagery from IRS series has been especially used because of 2.5-meter resolution and their affordable price.In order to orient satellite imagery with respect to the earth, ground control points (GCP) are designed and created in the region of interest.Having a stereo pair of images with near to full coverage; triangulation has been used for point distribution and block adjustment.The main calculations were performed by local triangulation software and we used a progressive control point number approach.Starting with a single control point, the amounts of vertical and horizontal accuracies are calculated and tabulated.
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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.973 | 0.969 |
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; both teacher heads agree on what is shown here.
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".