A structure–function based approach to floc hierarchy and evidence for the non-fractal nature of natural sediment flocs
Bibliographic record
Abstract
Natural sediment flocs are fragile, highly irregular, loosely bound aggregates comprising minerogenic and organic material. They contribute a major component of suspended sediment load and are critical for the fate and flux of sediment, carbon and pollutants in aquatic environments. Understanding their behaviour is essential to the sustainable management of waterways, fisheries and marine industries. For several decades, modelling approaches have utilised fractal mathematics and observations of two dimensional (2D) floc size distributions to infer levels of aggregation and predict their behaviour. Whilst this is a computationally simple solution, it is highly unlikely to reflect the complexity of natural sediment flocs and current models predicting fine sediment hydrodynamics are not efficient. Here, we show how new observations of fragile floc structures in three dimensions (3D) demonstrate unequivocally that natural flocs are non-fractal. We propose that floc hierarchy is based on observations of 3D structure and function rather than 2D size distribution. In contrast to fractal theory, our data indicate that flocs possess characteristics of emergent systems including non-linearity and scale-dependent feedbacks. These concepts and new data to quantify floc structures offer the opportunity to explore new emergence-based floc frameworks which better represent natural floc behaviour and could advance our predictive capacity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".