MétaCan
Menu
← Back to cohort
Record W7055541024

The concept of Emergy (spelled with “m”!) and the emergy analysis

2021· other· en· W7055541024 on OpenAlexaboutno aff

Bibliographic record

VenueCERN Document Server (European Organization for Nuclear Research) · 2021
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)EmergyPoint (geometry)Context (archaeology)Field (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

<!--HTML--> In this lecture, I will introduce the unifying concept of emergy (from EMbodied enERGY), starting from the pioneering work by Howard T. Odum. The hierarchical representation of emergy flows and energy transformation networks will be shown to be a natural description of the complexity of systems, able to capture the structural features from which the system dynamics arises and at the same time to point out the role of leverage points in determining the capability of a system to rearrange and reorganize upon perturbations. The basics of emergy analysis will be presented, along with the associated algebra. Francesco Gonella short bio PhD in Physics at University of Padova, Italy. Excéllence Postdoctorale at the Université Laval, Québec City, Canada. Since 2016, Full Professor of Physics at the Department of Molecular Sciences and Nanosystems of the Ca’ Foscari University of Venice. After twenty years of activity as an experimental physicist (study of nanostructured glasses, artistic glasses and glasses for energy technologies), ten years ago I have shifted my interests to Systems Thinking and Emergy Analysis, applied to the study of the functioning and the integrated sustainability of systems. I am Director of the “International School on Emergy Accounting'', and member of the Executive Council of the International Society for the Advancement in Emergy Research. I teach Sustainability and Systems Thinking at the Beijing Normal University in China as a high-end foreign expert, and keep courses on the same topics at the Universities of Turin and Catania in Italy. I was Visiting Professor at Tokyo Institute of Technology, Japan, for two years, and Visiting Researcher at Vanderbilt University (Nashville, USA) and the University of Florida (Gainesville, USA). H-index=37 (2020, Scopus). Author or co-author of almost 200 publications in international peer-reviewed Journals. Invited lecturer in Universities in Canada, USA, Germany, India, Japan, China, Poland, France.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.014
Scholarly communication0.0050.015
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0120.002

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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCERN Document Server (European Organization for Nuclear Research)→Same topicMagnetic confinement fusion research→French-language works237,207→