Representative point‐integrated suspended sediment sampling in rivers
Bibliographic record
Abstract
Abstract The vast majority of continental sediment delivered to the world's oceans moves by suspension in rivers. Depth‐integrated or point‐integrated bottle samplings are the traditional methods used to determine the mean concentration of suspended sediment in rivers. While there has been some investigation of the error associated with depth‐integrated sampling, the representativeness of a point‐integrated bottle sample has not been addressed in the literature. Here we analyze continuous hour‐long measurements of suspended sediment and grain‐size fractions collected using a LISST‐SL in the sand‐bed portion of the Fraser River, British Columbia, to determine an appropriate sampling time. The 2σ uncertainty range of individual 30 s samples varied from ±3% to ±33% about the observed mean, with a systematic increase toward the streambed. Mean concentrations for suspended sediment and grain‐size fractions were computed over increasing time periods and compared with a long‐duration mean concentration to determine when a sample becomes representative. A cumulative probability distribution was generated from multiple iterations of this process. All suspended sediment load and grain‐size fractions bear a low probability of representing the mean concentration over standard bottle sample durations. A probability >90% of representing the mean concentration and grain size of various fractions requires ∼570 s (9.5 min) of sampling. Sampling for a shorter period of 264 s (4.4 min) can yield a sample with 73% probability of representing the mean concentration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".