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Record W7096123696

RESEARCH RELATIVE TO LAND APPLICATION OF DIESEL INVERT DRILLING WASTES

2015· article· en· W7096123696 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicIrish and British Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDispose patternLand reclamationDiesel fuelSoil waterSoil qualityWork (physics)PetroleumDrilling
DOInot available

Abstract

fetched live from OpenAlex

Diesel invert wastes are one of the more difficult types of drilling mud wastes to dispose of due to the presence of hydrocarbons and salts. In 1989 a joint research program funded by the Alberta Research Council (ARC) and the Alberta Land Conservation and Reclamation Council was initiated to develop environmentally acceptable land application rates of diesel invert wastes for soils in Alberta and to model the fate of these wastes. The work was undertaken as four individual but closely linked experiments. This paper emphasizes the work related to the field study. The characterization experiment included the sampling and analysis of the waste materials and indigenous soils from the field site. The degradation experiment was undertaken to determine degradation rates of the oil, organic carbon and three targeted PAH’s (fluorine, phenanthrene, and pyrene) when applied to soil under laboratory conditions. The objective of the greenhouse pot experiment was to measure the relative impact of the oily materials and salts present in the invert wastes on soil quality and plant growth and quality.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.199
Threshold uncertainty score0.395

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.127
GPT teacher head0.425
Teacher spread0.298 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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Same topicIrish and British StudiesFrench-language works237,207