Distributions of Sulphate-reducing Bacteria Abundance in Surficial Sediments From the Canada Basin and Chukchi Sea
Bibliographic record
Abstract
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 ℃.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".