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SUCCESSFUL PRACTICES FOR LEARNING INFORMATION SYSTEMS DEVELOPMENT

2013· article· en· W22115082 on OpenAlexaboutno aff
Kalinka Kaloyanova

Bibliographic record

VenueINTED2013 Proceedings · 2013
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceKnowledge managementProcess managementBusiness

Abstract

fetched live from OpenAlex

Oxygenates present in gasoline, such as ethanol and MTBE, are a concern in subsurface contamination related to accidental spills. While gasoline hydrocarbon compounds have low solubility, MTBE and ethanol are more soluble, ethanol being completely miscible with water. Consequently, their fate in the subsurface is likely to differ from that of gasoline. To evaluate the fate of gasoline containing oxygenates following a release in the unsaturated zone shielded from rainfall/recharge, a controlled field test was performed at Canadian Forces Base Borden, in Ontario. 200L of a mixture composed of gasoline with 10% ethanol and 4.5% MTBE was released in the unsaturated zone, into a trench 20cm deep, about 32cm above the water table. Based on soil cores, most of the ethanol was retained in the source, above the capillary fringe, and remained there for more than 100 days. Ethanol partitioned from the gasoline to the unsaturated pore-water and was retained, despite the thin unsaturated zone at the site (~35cm from the top of the capillary fringe to ground surface). Due to its lower solubility, most of the MTBE remained within the NAPL as it infiltrated deeper into the unsaturated zone and accumulated with the gasoline on top of the depressed capillary fringe. Only minor changes in the distribution of ethanol were noted following oscillations in the water table. Two methods to estimate the capacity of the unsaturated zone to retain ethanol are explored. It is clear that conceptual models for sites impacted by ethanol-fuels must consider the unsaturated zone.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.538
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.005
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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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.

Study designNot applicable
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
Published2013
Admission routes1
Has abstractyes

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