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Supplementary material to "Variability in BVOC emissions and air quality impacts among urban trees in Montreal and Helsinki"

2025· preprint· en· W4411348426 on OpenAlexaboutno aff
Kaisa Rissanen, Juho Aalto, Jaana Bäck, Heidi Hellén, Toni Tykkä, Alain Paquette

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsAir quality indexEnvironmental scienceGeographyMeteorology

Abstract

fetched live from OpenAlex

Figure S1.Maps indicating the locations of the study trees in (a) Montreal and (b) Helsinki.The dashed lines outline the upscaling test areas for the upscaling BVOC emissions and ozone and secondary organic aerosol (SOA) formation by study species.AP, Acer platanoides; GT, Gleditsia triacanthos; PC, Populus canescens; QM, Quercus marcrocarpa; TC, Tilia cordata; BP, Betula pendula; QR, Quercus robur; TE, Tilia x europea; UG, Ulmus glabra.Table S1.The measurement date, ambient environmental conditions during measurements between 11.00 and 15.00 (T, temperature; RH, relative humidity), and the number, species, and locations of the sampled trees per day.For ambient temperatures and precipitation totals for the full months, see Supplement Fig. S4.AP, Acer platanoides; GT

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.787
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7870.192

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 head0.011
GPT teacher head0.268
Teacher spread0.258 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

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