Introducing “Embedded Toxicity”: A Necessary Metric for the Sound Management of Building Materials
Bibliographic record
Abstract
Global building and infrastructure stocks have increased 23-fold between 1900 and 2010, and are expected to grow further in the next decades, as will their already large environmental footprints. (1) The mining and manufacturing of building materials, for example, leads to significant greenhouse gas emissions, biodiversity loss, and diminished water quality and quantity. Recognizing these considerable impacts, action has been taken to address some aspects of the environmental footprints of building materials. For example, the concept of "embedded carbon" (also known as "embodied carbon"), or the upfront greenhouse gas emissions associated with producing building materials, has garnered attention as policymakers formulate plans for large-scale building renovations and retrofits to address the climate emergency. (2) Yet, building materials also mobilize numerous other chemicals with associated adverse human and/or environmental health impacts. Despite its centrality to the Sustainable Development Goals, the embedded toxicity of chemicals in building materials has not received the same attention as that of the embedded energy, water, and greenhouse gas emissions. We draw attention to the concept of "embedded toxicity" to aid in the sound management of building materials, as a needed addition to "embedded carbon".
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".