Whether interstitial space features were the main factors affecting sediment microbial community structures in Chaohu Lake
Bibliographic record
Abstract
Sediments cover a majority of Earth's surface and are essential for global biogeochemical cycles. The effects of sediment physiochemical features on microbial community structures have attracted attention in recent years. However, the question of whether the interstitial space has significant effects on microbial community structures in submerged sediments remains unclear. In this study, based on identified OTUs (operational taxonomic units), correlation analysis, RDA analysis, and Permanova analysis were applied into investigating the effects of interstitial space volume, interstitial gas space, volumetric water content, sediment particle features (average size and evenness), and sediment depth on microbial community structures in different sedimentation areas of Chaohu Lake (Anhui Province, China). Our results indicated that sediment depth was the closest one to the main environmental gradient. The destruction effects of gas space on sediment structures can physically affect the similarity of the whole microbial community in all layers in river dominated sedimentation area (where methane emits actively). However, including gas space, none of the five interstitial space parameters were significant with accounting for the microbial community structures in a sediment layer. Thus, except for the happening of active physical destruction on sediment structures (for example, methane ebullition), sediment interstitial space parameters were ineffective for affecting microbial community structures in all sedimentation areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".