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Record W2373548566

Distributions of Sulphate-reducing Bacteria Abundance in Surficial Sediments From the Canada Basin and Chukchi Sea

2007· article· en· W2373548566 on OpenAlexaboutno aff
Xuezheng Lin

Bibliographic record

VenueHaiyang kexue jinzhan · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentAbundance (ecology)BayOceanographyArcticLatitudeGeologyCanada BasinStructural basinEnvironmental scienceEcologyGeomorphologyBiology
DOInot available

Abstract

fetched live from OpenAlex

The surficial sediment samples from the Canada Basin and Chukchi Sea were collected during the second Chinese Arctic scientific expedition,the culture experiments on sulphate-reducing bacteria(SRB) in surficial sediments at 4 ℃ and 25 ℃ temperatures were made,and the distributions of SRB abundance in surficial sediments from the study area were studied in combination with the SRB study results for the first Chinese Arctic scientific expedition.It is shown from the study results that the SRB abundance cultured at 4 ℃ is in the range 0 to 2.4×104 cells/g(wet sample) with an anverge of 3 433 cells/g,and shows a distribution trend,that is,the SRB abundance is higher in low latitude area than in high latitude area,and higher in shallow water area than in deep water area.The SRB abudance in surficial sediments from the sea area is higher than those from the East China Sea,South China Sea and part of the Yellow Sea and lower than those from the Jiaozhou Bay and some sea areas of the western Arctic Ocean.The SRB abundance cultured at 25 ℃ is in the range 0 to 2.4×104 cells/g(wet sample) with an average of 4 062 cells/g,the SRB abundance cultured at 25 ℃ from water depth less than 1 880 m is consistent with that cultured at 4 ℃,but the SRB abundance cultured at 25 ℃ from water depth greater than 1 880 m is higher than that cultured at 4 ℃.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.609

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)0.0010.000

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 head0.007
GPT teacher head0.204
Teacher spread0.198 · 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.

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
Published2007
Admission routes1
Has abstractyes

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