MétaCan
Menu
Back to cohort
Record W2344806696 · doi:10.5539/ep.v5n1p92

Evaluation of Trace Metal Contamination in Ise Bay, Mie Prefecture, Central Japan, Based on Geochemical Analysis of Tidal Flat Sediments

2016· article· en· W2344806696 on OpenAlexvenueaboutno aff
Ibrahima M’Bemba Diallo, Hiroaki Ishiga

Bibliographic record

VenueEnvironment and Pollution · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsBaySedimentEstuaryTrace metalSiltIntertidal zonePollutionEnvironmental scienceBiotaEnrichment factorContaminationOceanographyGeologyHydrology (agriculture)Environmental chemistryHeavy metalsMetalGeomorphologyEcologyChemistry

Abstract

fetched live from OpenAlex

<span lang="EN-US">Geochemical analysis of tidal flat sediments was conducted to evaluate the environment of Ise Bay, Mie, central Japan. The sediment samples were analyzed using XRF to determine the geochemical compositions of sediments in the Ise and Matsusaka estuaries and their foreshores. Enrichment Factor (EF) and the Anthropogenic Contribution (AC) parameters were used to examine the potential effect of human activity. Furthermore, the Coastal Ocean Sediment Database, lowest and severe effect levels and Canadian Sediment Quality Guidelines were applied as benchmarks to assess the sediment quality. The results show that the highest average concentrations of metals occur in the Ise estuary, mainly due to the presence of higher proportions of silt and clay in samples at that site. The EFs of Pb in the Matsusaka foreshore, and that of As in the Ise foreshore reflect minimal pollution. The average AC ranged from 1 to 30%, implying that the lithology is the primary control of any enrichment in trace metals within the bay. The sediment quality guidelines indicate that the metal levels in the study areas do not constitute a major threat to biota.</span>

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 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 score0.485
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.242
Teacher spread0.228 · 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
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueEnvironment and PollutionSame topicHeavy metals in environmentFrench-language works237,207