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Record W2016137582 · doi:10.1016/j.envres.2014.10.012

Policy recommendations and cost implications for a more sustainable framework for European human biomonitoring surveys

2014· article· en· W2016137582 on OpenAlexfundno aff
Anke Joas, Lisbeth E. Knudsen, Marike Kolossa‐Gehring, Ovnair Sepai, Ludwine Casteleyn, Greet Schoeters, J. Angerer, Argelia Castaño, Dominique Aerts, Pierre Biot, Milena Horvat, Louis Bloemen, M. Fátima Reis, Ioana-Rodica Lupsa, Andromachi Katsonouri, Milena Černá, Marika Berglund, Pierre Crettaz, Péter Rudnai, Katarína Halzlová, Maurice Mulcahy, Arno C. Gutleb, Marc Fischer, Georg Becher, Nadine Fréry, Génon K. Jensen, Lisette van Vliet, Holger M. Koch, Elly Den Hond, Ulrike Fiddicke, Marta Esteban, Karen Exley, Gerda Schwedler, Margarete Seiwert, Danuta Ligocka, Philipp Hohenblum, Soterios Α. Kyrtopoulos, Maria Botsivali, Elena DeFelip, Claude Guillou, Fabiano Reniero, Regina Gražulevičienė, Toomas Veidebaum, Thit Aarøe Mørck, Jeanette K.S. Nielsen, Janne Jensen, Teresa C. Rivas, Jinny Sánchez, Gudrun Koppen, Roel Smolders, Szilvia Középesy, Adamos Hadjipanayis, Andrea Krsková, J. Aleksandra Fucic, José Pereira Miguel, Anca Elena Gurzău, Michal Jajcaj, Darja Mazej, Janja Snoj Tratnik, Andrea Lehmann, Kristin Larsson, Birgit Dumez, Reinhard Joas

Bibliographic record

VenueEnvironmental Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsnot available
FundersHealth CanadaCenters for Disease Control and PreventionBundesamt für UmweltUmweltbundesamtInstitut de Veille SanitaireBundesamt für GesundheitVlaamse Instelling voor Technologisch OnderzoekVrije Universiteit Brussel
KeywordsComparabilityBiomonitoringEuropean unionEuropean commissionStakeholderEnvironmental resource managementBusinessAction planSustainabilityEnvironmental planningEnvironmental healthPolitical scienceMedicineEnvironmental sciencePublic relationsEcology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.107
metaresearch head score (Gemma)0.206
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: Empirical · Consensus signal: none
Teacher disagreement score0.107
Threshold uncertainty score0.567

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1070.206
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0040.005
Science and technology studies0.0030.006
Scholarly communication0.0120.010
Open science0.0070.008
Research integrity0.0350.011
Insufficient payload (model declined to judge)0.0370.003

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 head0.090
GPT teacher head0.446
Teacher spread0.356 · 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
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

Citations19
Published2014
Admission routes1
Has abstractno

Explore more

Same venueEnvironmental ResearchSame topicHealth, Environment, Cognitive AgingFrench-language works237,207