Preface: Workshop “Laser Scanning 2023”
Bibliographic record
Abstract
Laser scanning 2023 is the 12th workshop of a series of ISPRS workshops covering various aspects of space borne, airborne, mobile and terrestrial laser scanning in both indoor and outdoor environments. The workshop brings together experts focusing on applying and processing point cloud data acquired from laser scanners and other active 3D imaging systems, including terrestrial, mobile, aerial, unmanned-aerial and space-borne sensing platforms. Topics include all aspects related to sensor calibration, data acquisition and data processing including notably registration, feature extraction, object detection, 3D modelling, BIM and change analysis. The workshop is part of the ISPRS Geospatial Week 2023 in Cairo held in parallel with a number of related geospatial workshops. Laser Scanning is a central topic in the ISPRS community and is principally organized by the working group WG II/2, Point Cloud Generation and Processing.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.226 | 0.148 |
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".