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Record W2785550642 · doi:10.1039/c7pp90043k

Environmental effects of ozone depletion, UV radiation and interactions with climate change: UNEP Environmental Effects Assessment Panel, update 2017

2018· article· en· W2785550642 on OpenAlexaffabout
Alkiviadis Bais, Robyn Lucas, Janet F. Bornman, Craig E. Williamson, Barbara Sulzberger, Amy T. Austin, Stephen R. Wilson, Anthony L. Andrady, G. Bernhard, Richard McKenzie, P. J. Aucamp, S. Madronich, Rachel Ε. Neale, Seyhan Yazar, Antony R. Young, Frank R. de Gruijl, Mary Norval, Y. Takizawa, Paul W. Barnes, T. Matthew Robson, Sharon A. Robinson, C. L. Bailaré, Stephan D. Flint, Patrick J. Neale, Samuel Hylander, Kevin C. Rose, Sten‐Åke Wängberg, Donat‐Peter Häder, Robert C. Worrest, Richard G. Zepp, N. D. Paul, Rose M. Cory, Keith R. Solomon, Janice Longstreth, Krishna K. Pandey, Halim Hamid Redhwi, Ayako Torikai, Anu Heikkilä

Bibliographic record

VenuePhotochemical & Photobiological Sciences · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Guelph
FundersNational Health and Medical Research CouncilMedical Research CouncilUniversity of MiamiNew Zealand GovernmentUniversity of WollongongDivision of Environmental BiologyMinistry of Business, Innovation and EmploymentHavs- och VattenmyndighetenBundesministerium für Umwelt, Naturschutz und ReaktorsicherheitGeneral Secretariat for Research and TechnologyHelsingin YliopistoU.S. Environmental Protection AgencySmithsonian InstitutionNaturvårdsverketNational Science Foundation
KeywordsMontreal ProtocolOzone layerClimate changeOzone depletionEnvironmental scienceUltraviolet radiationOzoneMeteorologyClimatologyGeographyEcologyChemistryBiology

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)0.004
metaresearch head score (Gemma)0.008
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.261
Threshold uncertainty score0.519

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.008
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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

Citations351
Published2018
Admission routes2
Has abstractno

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