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Record W4250993923 · doi:10.11647/obp.0193.06

Carbon

2020· book-chapter· en· W4250993923 on OpenAlexaff
David Archer

Bibliographic record

VenueOpen Book Publishers · 2020
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsAlberta Oil Sands Technology and Research AuthorityUniversity of British Columbia
Fundersnot available
KeywordsEarth scienceGlobal warmingGreenhouse gasCarbon cycleAtmospheric carbon cycleGreenhouse effectEarth (classical element)Carbon fibersEnvironmental scienceNatural resource economicsClimate changeCarbon dioxideCarbon sequestrationEcologyGeologyMaterials scienceBiologyEconomics

Abstract

fetched live from OpenAlex

This chapter elucidates the importance of carbon to the Earth system and outlines the global debate on its use and impact. The element manifests itself through a number of reservoirs: the crust and mantle (the overwhelming majority), dissolved forms of inorganic carbon, living organic material and atmospheric trace gases (including the infamous 'greenhouse' variety), representing just 0.00064%, but responsible for the absorption of out-going infra-red radiation from Earth's surface. The chapter then outlines how variations in Earth’s atmospheric levels of CO2 and methane are related to the exchange of carbon between these reservoirs, which combine to act as a global thermostat over geological timescales. These processes have not always been human-induced: the Earth’s history is peppered with periods of volcanic activity, resulting in wild extremes in global temperature. However, the crucial difference in such phenomena and human-induced carbon is the abrupt ‘gorging’ and ‘dumping’ we are engaged in over a relatively minute timeframe, hindering ocean acidity levels in seas from rebalancing. As the first agent sentient of the effect it is having on the Earth’s metabolism, humanity must make unprecedented changes in its relationship to carbon. As the chapter points out, dramatic changes have been enacted before, when urbanized communities have been faced with the consequences of poor sanitation and pollution. However, decisions dictated by market economics or arbitrary, politically-derived thresholds risk making the rate of change simply too slow. The fundamental realization is that our relationship to energy, and where this energy is sourced, must change, fast.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0430.002

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.

Opus teacher head0.012
GPT teacher head0.195
Teacher spread0.183 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

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