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Record W2727275208 · doi:10.1016/j.envres.2017.06.028

Exploring pathways linking greenspace to health: Theoretical and methodological guidance

2017· review· en· W2727275208 on OpenAlexaff
Iana Markevych, Julia Schoierer, Terry Hartig, Alexandra Chudnovsky, Perry Hystad, Angel M. Dzhambov, S. de Vries, Margarita Triguero‐Mas, Michael Bräuer, Mark Nieuwenhuijsen, Gerd Lupp, Elizabeth Richardson, Thomas Astell‐Burt, Donka Dimitrova, Xiaoqi Feng, Maya Sadeh, Marie Standl, Joachim Heinrich, Elaine Fuertes

Bibliographic record

VenueEnvironmental Research · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of British Columbia
FundersHorizon 2020National Health and Medical Research CouncilH2020 Marie Skłodowska-Curie ActionsNational Heart Foundation of AustraliaGeneralitat de CatalunyaEuropean Cooperation in Science and Technology
KeywordsBiopsychosocial modelPopulation healthContext (archaeology)CausationHarmPopulationEnvironmental planningPsychologyEnvironmental healthGeographyMedicineSocial psychologyPolitical science

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)0.010
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0080.013
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0030.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.718
GPT teacher head0.515
Teacher spread0.203 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2,422
Published2017
Admission routes1
Has abstractno

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