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Record W2530887172 · doi:10.1177/0734242x16670000

Characterisation of excavated fine fraction and waste composition from a Swedish landfill

2016· article· en· W2530887172 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

VenueWaste Management & Research The Journal for a Sustainable Circular Economy · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsUniversité de Montréal
FundersKauno Technologijos Universitetas
KeywordsFraction (chemistry)Environmental chemistryEnvironmental scienceMoistureMass fractionOrganic matterTotal organic carbonZincHeat of combustionChemistryWaste managementMineralogyMetallurgyCombustionMaterials science

Abstract

fetched live from OpenAlex

The present research studies the characterisation and the physico-chemical properties of an excavated fine fraction (<10 mm) from a Swedish landfill, the Högbytorp. The results showed that the fine fraction represents 38% by mass of the total excavated wastes and it contains mainly soil-type materials and minerals. Higher concentrations of zinc, copper, barium and chromium were found with concentrations higher than the Swedish Environmental Protection Agency (EPA) for contaminated soil. The found moisture and organic contents of the fine fraction were 23.5% and 16.6%, respectively. The analysed calorific value (1.7 MJ kg −1 ), the potential of CH 4 (4.74 m 3 t −1 dry matter) and Total Organic Carbon (TOC) (5.6%) were low and offer low potential of energy. Sieving the fine fraction further showed that 80% was smaller than 2 mm. The fine represents a major fraction at any landfill (40%–70%), therefore, characterising the properties of this fraction is essential to find the potential of reusing/recycling or safely redisposing.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.274
Teacher spread0.253 · 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