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Record W1198903472 · doi:10.1520/stp11569s

Assessment of the Lead Release from Cables Buried in Sediments into the Water Column

2003· book-chapter· en· W1198903472 on OpenAlexaff
Анна Давыдовна Дегтярева, Maria Elektorowicz, T Ebadi

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsConcordia University
Fundersnot available
KeywordsWater columnLead (geology)Column (typography)GeologyEnvironmental scienceOceanographyEngineeringPaleontologyTelecommunications

Abstract

fetched live from OpenAlex

The study investigates the change in the water column quality in a situation where submarine communication cables cross the water bodies. In spite of precautions in technological achievements it is possible that these cables can be subjected to potential corrosion processes. This study was performed for a particular area of the St. Lawrence River. The impact of cables buried in sediments on lead speciation in the water column was shown under different conditions (anaerobic and aerobic, at different partial pressures of CO2, presence of organic acids). Equilibrium in the water was calculated taking into account gas, water and solid phases. Programs Equilibrium and EPH from FACT were utilized in this study to calculate Eh-pH diagrams and the equilibrium in water. The main form of lead was found to be PbOH+ (3.34×10-9 M/kg), the concentration of the free lead ion was two orders of magnitude lower (2.5×10-11 M/kg). The calculations, in which solid phases where considered, demonstrated that the water was oversaturated with respect to dolomite and iron(III) hydroxide. It was speculated that under these conditions dolomite could form a protective layer around the cable, which can delay the corrosion. Under anoxic conditions cerussite and lead sulfide precipitated. When PCO2 increased, the concentration of the free ion of lead increased.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0110.000

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 head0.211
Teacher spread0.202 · 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 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
Published2003
Admission routes1
Has abstractyes

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