3D GIS modelling of road and building material stocks: A case study of Grenada
Bibliographic record
Abstract
Quantifying and mapping material stocks is crucial for built environment stock management and sustainable planning. This study presents a Geographic Information System (GIS)-based bottom-up approach for modelling road and building material stocks in Grenada, a small island state. Light Detection and Ranging (LiDAR) data were utilized to estimate building heights and building stocks. The first 3D WebGIS application was developed for Grenada to visualize material stocks in 3D city models. The road stocks in Grenada were estimated to be 4375 kilo tonnes (40.96 t/capita) in 2015, about one-third of building stocks, highlighting the importance of infrastructure stocks in small island states. LiDAR-derived building heights were more accurate, estimating building stocks 4.8 % lower than occupancy class-based height assumptions in the sample site. This study develops Grenada’s first road stock account and assesses a novel methodology for estimating building stocks in small island states, offering insights for enhancing resource assessment and management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".