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Record W4410580046 · doi:10.1016/j.jclepro.2025.145763

Landfill footprint geometrical design evolution and land surface thermal heterogeneity

2025· article· en· W4410580046 on OpenAlexafffund
Arash Gitifar, Nima Karimi, Sharmin Jahan Mim, Farzin Naghibalsadati, Kelvin Tsun Wai Ng

Bibliographic record

VenueJournal of Cleaner Production · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFootprintEnvironmental scienceEcological footprintThermalSurface (topology)Land useEnvironmental engineeringEarth scienceEnvironmental resource managementCivil engineeringGeologyEngineeringGeographySustainabilityMeteorologyEcologyGeometryMathematics

Abstract

fetched live from OpenAlex

The evolution of landfill footprint design and the associated environmental impacts have been mostly ignored in literature. This study examines landfill footprint geometrical shape using four different shape factors, including compactness (CS), rectangularity (RS), stretch (SS), and ease of access (ES) on 99 active landfills in UK. ES had the highest mean value of 0.69, while SS had the lowest mean value of 0.24. RS and CS showed similar mean values of 0.55 and 0.53. The footprints are categorized based on the landfill’s age into “young”, “intermediate” and “old” groups to assess the evolution of landfill design. Younger landfills exhibit the lowest compactness (median CS = 0.52), followed by intermediate (median CS = 0.55) and older sites (median CS = 0.62). In terms of RS, all the sites showed median values around 0.55. The young landfills have the lowest SS with a median of 0.20, while the old and intermediate groups had medians of 0.23 and 0.24 respectively. The variabilities of sites’ land surface temperature were also assessed. This study underscores the role of landfill footprint design on landfill operation and introduces new aspects of design strategies to mitigate environmental risks.

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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.818
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.248
Teacher spread0.231 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2025
Admission routes2
Has abstractyes

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