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Record W2327738360 · doi:10.1021/es102273t

Patterns of Iron Use in Societal Evolution

2010· article· en· W2327738360 on OpenAlexaboutno aff
Daniel B. Müller, Tao Wang, Benjamin D. Duval

Bibliographic record

VenueEnvironmental Science & Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsEnvironmental scienceBusinessEnvironmental planning

Abstract

fetched live from OpenAlex

A dynamic material flow model was used to analyze the patterns of iron stocks in use for six industrialized countries. The contemporary iron stock in the remaining countries was estimated assuming that they follow a similar pattern of iron stock per economic activity. Iron stocks have reached a plateau of about 8-12 tons per capita in the United States, France, and the United Kingdom, but not yet in Japan, Canada, and Australia. The global average iron stock was determined to be 2.7 tons per capita. An increase to a level of 10 tons over the next decades would deplete about the currently identified reserves. A subsequent saturation would open a long-term potential to dramatically shift resource use from primary to secondary sources. The observed saturation pattern implies that developing countries with rapidly growing stocks have a lower potential for recycling domestic scrap and hence for greenhouse gas emissions saving than industrialized countries, a fact that has not been addressed sufficiently in the climate change debate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.007
GPT teacher head0.222
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations196
Published2010
Admission routes1
Has abstractyes

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Same venueEnvironmental Science & TechnologySame topicHeavy metals in environmentFrench-language works237,207