Substitution between floor constructions in wood and natural stone: comparison of energy consumption, greenhouse gas emissions, and costs over the life cycle
Bibliographic record
Abstract
This paper compares two floor constructions used at the new airport outside Oslo, one made of solid oak and one made of natural stone, to (i) make an inventory of energy consumption and greenhouse gas (GHG) emissions over the life cycle of the two constructions, (ii) calculate the differences regarding GHG emissions and cost, and (iii) determine which factors have the strongest influence on the results. Manufacturing the wood floor required 1.6 times more energy and produced one-third of the GHG emissions compared with the natural stone floor. Over the life cycle, net GHG emissions can be avoided only if the wood is used as a biofuel after the replacement or demolition of the floor. The wooden floor must be competitive on price to be a cost-efficient action against global warming. Per cubic metre of wood floor, emissions of up to 1.263 t of CO2 equivalents can be avoided by a substitution between the two floor constructions. The factors that have the most influence on the result are carbon fixation on forest land, waste handling of wood, and discount rate, the latter reflecting the relative importance over time given to a unit of GHG emissions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".