A Holistic Framework for Estimation of Aggregate Inventory for Road Construction Using Google Earth Pro®: A Case Study of Libya State
Bibliographic record
Abstract
Abstract In terms of non-fuel minerals, natural aggregates are the world's most valuable and are the third most consumed resource worldwide after water and air. Despite the availability of materials, transportation agencies design their roads according to traffic levels and soil conditions. In many cases, a national inventory is not carried out thoroughly. Therefore, a preliminary estimate of a quarry's capacity for a road project generally will not be available or accurate until after the contract has been awarded. It is estimated that thousands of kilometers of roads will need to be constructed in the developing world in the coming decades in order for many economies to develop. In order to optimize the use of quality aggregates, it is important to identify and catalog all existing road construction materials in those countries by performing an inventory of available source materials such as sand and gravel. In this study, Google Earth Pro® software is used to estimate a potential aggregate inventory using national data publications on a case study of Libya. Based on the study results, there are 38.3 million tons of aggregates available for road construction from 21 active quarries. To sum up, sustainable road design would greatly benefit from this information, helping to reduce aggregates, costs, time, and air pollution.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| 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".