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Record W4311325337 · doi:10.54097/hset.v25i.3490

The Main Factors of Environmental Impact of the Olympic Games

2022· article· en· W4311325337 on OpenAlexaff
Yuning Yang

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEcological footprintFootballFocus (optics)Field (mathematics)Environmental impact assessmentEnvironmental planningEnvironmental resource managementPolitical scienceSustainable developmentGeographyEnvironmental science

Abstract

fetched live from OpenAlex

This paper analyzes the main environmental factors in the current period, focusing on some main man-made environmental impacts during major sports events. For these problems, we will use the method of ecological footprint to explore specific directions and causes. How consumers can properly use land resources to achieve environmentally friendly guidelines is a goal to be explored. At the same time, this paper will pay more attention to whether the environment people live in the society will have a certain impact on nature, mainly focusing on some studies in the living area. However, ecological footprint is not the only comprehensive measurement method, there are some limitations. This article will not focus on any one Olympic Games, but will analyze and discuss different Olympic Games in different periods for the same problem. The main analysis of the article will have a lot to do with the football field, and some references will be made here because our focus is not just on the Olympic Games.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.006
GPT teacher head0.238
Teacher spread0.232 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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