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Record W2093567136 · doi:10.4141/cjss07043

Carbon dioxide emissions by urban turfgrass areas

2008· article· en· W2093567136 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Soil Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLawnEnvironmental scienceCarbon dioxideGreenhouse gasFlux (metallurgy)Carbon fibersCarbon cycleEnvironmental chemistryAgronomyChemistryEcologyEcosystemBiologyMathematics

Abstract

fetched live from OpenAlex

This research compared four turfgrass lawn management approaches on CO 2 emissions: (1) fertilized and frequently mowed with clippings removal, and unfertilized with clippings left on site and mowed 2) weekly, 3) three times, or 4) once during the growing season. CO 2 emissions were measured weekly with flux chambers. Mowing frequency had higher impact on CO 2 flux than fertilisation and soil characteristics. Frequently mowed sites emitted CO 2 at a maximum rate of 0.63 mg m -2 s -1 and annually up to 2.0 kg m -2 , an emission four times higher than lawns mowed infrequently. Differences between treatments mostly occurred during warm weeks. Key words: Lawns, greenhouse gases, carbon cycle, carbon dioxide, net CO 2 exchange

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.195
Teacher spread0.184 · 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