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Record W4396736129 · doi:10.5194/egusphere-2024-1133

Resolving the Technosphere

2024· preprint· en· W4396736129 on OpenAlexafffund
Eric D. Galbraith, Abdullah-Al- Faisal, Tanya Matitia, William Fajzel, Ian Hatton, Helmut Haberl, Fridolin Krausmann, Dominik Wiedenhofer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicOrigins and Evolution of Life
Canadian institutionsMcGill University
FundersH2020 European Research CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPsychology

Abstract

fetched live from OpenAlex

Abstract. The global assemblage of human-created buildings, infrastructure, machinery and other artifacts has been called the `technosphere', and plays a major role in the present-day dynamics of the Earth system. It enables the rapid extraction and processing of materials from other spheres, combusts fossil fuels causing climate change, and transports materials and people across the planet surface. It provides a vast range of services to humans, such as supporting the production of food, shelter, long-distance communication, and entertainment. However, Earth system science has been slow to explicitly incorporate the technosphere as an integrated part of its conceptual and quantitative frameworks. Here we propose a refined definition of the technosphere, intended to assist in developing functional integration with other Earth system spheres, and an End-Use Technosphere categorization, EUTEC, that is theoretically aligned with human activities and wellbeing. The formal definition and resolved categorization enable basic attributes of the technosphere to be delineated, including its mass distribution among components and in space, as well as its temporal dynamics. In particular, of the roughly 1 Tt of technosphere mass, we estimate that one third is comprised of residential buildings and one third by the transportation system, both of which we map at one-degree resolution. Moreover, we show that reconstructions of technosphere mass since 1900 follow exponential growth with long-run growth rates of >3 % y-1, consistent with autocatalytic behaviour, allowing it to become an ever-more dominant component of the Earth system. The quantitative understanding of the technosphere remains rudimentary, and is in great need of further work to better integrate it with Earth system science.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.997

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 communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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 teacher head, not a consensus.

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

Citations4
Published2024
Admission routes2
Has abstractyes

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