Determination of the Settling Rate of Clay/Cyanobacterial Floccules
Bibliographic record
Abstract
The mechanisms underpinning the deposition of fine-grained, organic-rich sediments are still largely debated. Specifically, the impact of the interaction of clay particles with reactive, planktonic cyanobacterial cells to the sedimentary record is under studied. This interaction is a potentially major contributor to shale depositional models. Within a lab setting, the flocculation and sedimentation rates of these materials can be examined and measured in a controlled environment. Here, we detail a protocol for measuring the sedimentation rate of cyanobacterial/clay mixtures. This methodology is demonstrated through the description of two sample experiments: the first uses kaolin (a dehydrated form of kaolinite) and Synechococcus sp. PCC 7002 (a marine coccoid cyanobacteria), and the second uses kaolin and Synechocystis sp. PCC 6803 (a freshwater coccoid cyanobacteria). Cyanobacterial cultures are mixed with varying amounts of clay within a specially designed tank apparatus optimized to allow continuous, real-time video and photographic recording. The sampling procedures are detailed as well as a post-collection protocol for precise measurement of chlorophyll a from which the concentration of cyanobacterial cells remaining in suspension can be determined. Through experimental replication, a profile is constructed that displays sedimentation rate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".