Network Fiber Costing Accuracy Through Geospatial and Non-Planar Terrain Management
Bibliographic record
Abstract
Telecom Network planning and construction that comes under the purview of Outside Plant Engineering is one of the most critical functions of a Telecom service provider. Accurate estimation of fiber cable length and cost management during Network planning is critical for the successful deployment of fiber networks. However, issues arise in costs and scheduled deployments due to discrepancies between planned and actual lengths leading to delays in network deployment and reduced customer satisfaction. One of the key reasons for this is the differences in topography and non-planar terrain that would result in changes in fiber estimations, splitters and the planned network topology. This can lead to significant cost overruns and project delays. This paper explores the challenges associated with these discrepancies, the importance of path creation, and proposes methods to improve estimation accuracy using the Geodesic DEM dataset. The paper proposes ways in which a 2D plan can be transformed into a 3D plan (a non-planar terrain), thereby giving a better length estimation. The authors also explore other alternative methods and the future extensions to the proposed methodology.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".