How timber can decarbonize the built environment
Bibliographic record
Abstract
Fact: per square metre buildings have the potential capacity to store more carbon than forests. It is difficult to find people who do not like trees. Trees have cheerleaders across the social classes; young and old, rich and poor, left and right. There is a vocal minority who believe that trees should be left well alone. What, they ask, is the environmental gain of chopping them down and bringing them out of the forest? Well, the answer is that when it comes to tackling climate change our forests are only half the story – and hence only half the answer. We can build with wood, wood that stores carbon and substitutes for much more carbon-intensive building materials. Without doubt wood is special. As one of the champions of building with wood, Vancouver-based architect Michael Green, has put it: “Wood is the most technologically advanced material I can build with. It just happens to be that Mother Nature holds the patent on it and we are not comfortable with that. But that's the way it should be: nature's fingerprints in the built environment”. We have been building houses from wood for thousands of years. Most European cities still have a few timber-framed buildings dating back 300 years or more. York in the UK is a good example. Here you will find The Shambles – a street of medieval timber-framed buildings with a strong Harry Potter feel to them. Strasbourg, home of one of the two seats of the European Parliament – and somewhere I travelled to every month for five years as an MEP – has some fine examples of old timber-framed buildings (Figure 2.1). Some of these were carefully rebuilt after the Second World War but to the untrained eye look as old as those that were not damaged. When these buildings were erected, they had only one purpose – that of providing shelter, a home. However, although unknown at the time, they were also safely storing the carbon that the timber had sequestrated (soaked up) when it was growing as a tree in the forest.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".