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Record W4298110301 · doi:10.3390/su141912423

Development of an Urban Turfgrass and Tree Carbon Calculator for Northern Temperate Climates

2022· article· en· W4298110301 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.

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsLandscape Alberta Nursery Trades AssociationUniversity of Guelph
FundersTexas Tech University
KeywordsCarbon sequestrationCalculatorCarbon fibersTemperate climateEnvironmental scienceUrban ecosystemCarbon fluxAgroforestryEcosystemNatural resource economicsEcologyUrban planningComputer scienceCarbon dioxideBiologyEconomics

Abstract

fetched live from OpenAlex

The presence of urban plants in an ecosystem are vital for processes including carbon sequestration and the type of urban plants included in urban settings affect the amount of carbon sequestered. The objective of this study is to assess the ability of urban plants to sequester carbon under a number of available management practices through the development and refinement of an accessible carbon calculator. Available urban plant data were analyzed using the calculator developed using available literature regarding carbon sequestration to determine differences between different types of plants, when hidden carbon costs (HCC) were considered. Carbon sequestration including HCC for turfgrasses could be calculated but there was a lack of information regarding HCC of urban trees and shrubs. The calculator was shown to be an effective tool for homeowners to determine viable management practices to maintain or increase carbon sequestration.

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 score0.061
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.234
Teacher spread0.227 · 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