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Record W2750586032 · doi:10.1021/acs.est.7b02155

Evidence of Coupled Carbon and Iron Cycling at a Hydrocarbon-Contaminated Site from Time Lapse Magnetic Susceptibility

2017· article· en· W2750586032 on OpenAlexfundno aff
Anders L. Lund, Lee Slater, Estella A. Atekwana, Dimitrios Ntarlagiannis, Isabelle M. Cozzarelli, B. Bekins

Bibliographic record

VenueEnvironmental Science & Technology · 2017
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
FundersEnbridgeU.S. Geological SurveyMinnesota Pollution Control Agency
KeywordsFerrihydriteSideriteContaminationEnvironmental chemistryEnvironmental scienceMagnetiteCarbon cycleGeologySoil scienceChemistryMineralogyEcology

Abstract

fetched live from OpenAlex

Conventional characterization and monitoring of hydrocarbon (HC) pollution is often expensive and time-consuming. Magnetic susceptibility (MS) has been proposed as an inexpensive, long-term monitoring proxy of the degradation of HC. We acquired repeated down hole MS logging data in boreholes at a HC-contaminated field research site in Bemidji, MN, USA. The MS data were analyzed in conjunction with redox conditions and iron availability within the source zone to better assess whether MS can serve as a proxy for monitoring HC contamination in unconsolidated sediments. The MS response at the site diminished during the sampling period, which was found to coincide with depletion of solid phase iron in the source zone. Previous geochemical observations and modeling at the site suggest that the most likely cause of the decrease in MS is the transformation of magnetite to siderite, coupled with the exhaustion of ferrihydrite. Although the temporal MS response at this site gives valuable field-scale evidence for changing conditions of iron cycling and stability of iron minerals it does not provide a simple proxy for long-term monitoring of biodegradation of hydrocarbons in the smear 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.999

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 studies0.0010.003
Scholarly communication0.0000.000
Open science0.0020.003
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 head0.207
Teacher spread0.200 · 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 designBench or experimental
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

Citations15
Published2017
Admission routes1
Has abstractyes

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