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Record W2790426567 · doi:10.1002/env.2486

A window on The International Environmetrics Society: The first 25 international conferences

2018· article· en· W2790426567 on OpenAlexafffund
Sylvia R. Esterby

Bibliographic record

VenueEnvironmetrics · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersUniversity of British ColumbiaCommonwealth Scientific and Industrial Research Organisation
KeywordsPolitical scienceContext (archaeology)Order (exchange)Library scienceSociologyPublic relationsHistoryComputer scienceBusiness

Abstract

fetched live from OpenAlex

The 25th international conference of The International Environmetrics Society (TIES) was held in 2015. The conferences have been an integral part of the development of TIES and of the society's official journal, Environmetrics, since the concurrent inception of the society and journal at the first conference in 1989. The conferences are documented in chronological order, with more description of the early conferences because many features were set at these conferences. Some common features are included for all conferences, and only new or particularly relevant components are mentioned for later conferences. This development is then drawn on to discuss the character, contributors, and evolution of the conferences in the context of TIES as a society with the aim of fostering the development and use of quantitative methods in solving environmental problems through collaboration among individuals in different disciplines and types of organizations.

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)0.017
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.010
Science and technology studies0.0070.002
Scholarly communication0.0300.014
Open science0.0020.009
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0620.013

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 head0.042
GPT teacher head0.258
Teacher spread0.217 · 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.

Study designQualitative
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

Citations1
Published2018
Admission routes2
Has abstractyes

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