Analysis of spatial uncertainty in LiDAR-derived building data and uncertainty propagation in modeling of urban atmospheric dispersion
Bibliographic record
Abstract
Results of environmental models (EMs) are often used to assist decision making. However, EM outcomes vary significantly with different input data, model parameters and model assumptions. Therefore, informed decision making requires an in-depth understanding of how the changes in input data, model parameters and model assumptions influence the model outputs. While EMs are now accustomed to geo-spatial data, the influences of spatial uncertainty are often overlooked. This research examines the influence of spatial uncertainty throughout the three stages of general environment modeling: (1) examine the uncertainty in geo-spatial data as representation of the environment, (2) examine the uncertainty in the linkage between EMs and Geographic Information System (GIS) and, (3) examine and compare the influence of spatial uncertainty with the uncertainty of model parameters. LiDAR data and urban atmospheric dispersion model (UADM) are used as a use case, to demonstrate the methods and benefits of examining the influence of spatial uncertainty toward EMs.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".