Reduction of Greenhouse Gas Emissions Through Utilization of Turf and Root Reinforcement Mat (TRM) Technology in Green Armouring of Civil Structures as Compared to Traditional Rock Armouring
Bibliographic record
Abstract
Carbon footprint impact and the reduction of greenhouse gas emissions is beginning to pervade modern civil construction and operational practices. Designers, owners, communities and nations are struggling to incrementally reduce greenhouse gas emissions. Green armouring of civil structures, historically hard armoured in rock, holds promise for significant reduction of the carbon footprint of construction. This paper explores the reduction of greenhouse gas emissions by utilizing Turf and Root Reinforcement Mat (TRM) technology in armouring civil structures as compared to traditional rock armouring. The paper outlines the generally accepted use of geosynthetics in civil structure construction, and surveys the current greenhouse gas reduction strategies in construction. A case study of the Assiniboine River Diversion Failsafe in Manitoba, Canada outlines successes in the armouring of a civil structure against erosion utilizing TRM technology. A comparative analysis explores the carbon footprint of constructing with this green technology as compared to hard armouring with conventional construction methods such as rock riprap and rock armouring. Highlighted is the significant carbon footprint efficiency of this Turf and Root Reinforcement Mat green armouring technique. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".