Hyporheic exchange inside a flat gravel bed, flume experiments and modelling.
Bibliographic record
Abstract
The aim of this research is to gain more understanding on the parameters that affect hyporheic\nexchange. Hyporheic exchange is a diverse process, in which many different parameters\naffect the hyporheic flow paths and residence times. Literature research has showed that\nhyporheic exchange is in most cases mainly dependant on the channel morphology, the\nsediment characteristics of hyporheic zone and the flow velocity of the stream. Many other\nfactors also affect hyporheic exchange, such as lateral groundwater inflows and natural\nobstructions. The diversity of all of these factors makes it hard to predict hyporheic exchange\nthrough models. The advective pumping model has shown that it is able to predict exchange\ndue to bedforms well. This contributed to different studies, which allowed the prediction of\nvertical hyporheic exchange fluxes. But lateral hyporheic exchange fluxes still can not be\npredicted.\nFlume experiments were conducted at the Quesnel River Research Centre (QRRC) in Likely,\nBC, Canada, to provide more insight on the pattern of hyporheic exchange. EC-meters were\nplaced inside a flat gravel bed with a d50 of 25.28 mm for the top layer, and 22.90 mm for the\nbottom layer. Experiments were performed with different flow velocities and water levels.\nThese experiments showed the irregularities in hyporheic exchange in a heterogeneous gravel\nbed and provided some insight in the pattern of hyporheic exchange. To further investigate\nsome of the unknown variables, an advection-diffusion model was made. By means of this\nmodel, the effects of variations in flow velocity and water level were studied.\nThe following conclusions were made:\n· An increase in flow velocity results in smaller residence times, and an increase in\nwater level results in larger residence times.\n· Water level has a relatively large impact on the reach of the hyporheic exchange into a\nflat gravel bed.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".