Quantification of Advective Transport Phenomena to Better Understand Dispersion in the Field
Bibliographic record
Abstract
Observation of dispersion in field situations has left three issues that may be better understood by applying advective transport phenomena. (1) In some experiments, the longitudinal dispersivity becomes constant with increasing pathlength and in other cases it remains growing. (2) Dispersivities reported from multiple comprehensive observations at a single site differ at similar pathlength in some cases more than a factor two. (3) The observed difference between the plume fronts and plume tails is not represented in the reported parameters. The analytic equations for advective transport phenomena at macroscale of De Lange (2020) describe the thickness of the affected flow-tube and the spread of the plume front and tail. The scale factor defines the size of the averaging domain and so of the initial phase. The new macroscale correlation coefficient relates the growth of the longitudinal dispersivity beyond the initial phase to the aquifer heterogeneity. Using stochastic parameters for the aquifer heterogeneity, the parameters are quantified at 14 field experiments in the United States, Canada and Europe enabling the comparison of calculated and reported final dispersivities. Using the quantified parameters, 146 reported and calculated dispersivities along the traveled paths show a good match. A dispersivity derived from the local plume growth may differ a factor of two from the aquifer-representative value. The growths of plume fronts and tails between two plume stages are assessed in 14 cases and compared to calculated values. Distinctive parameters for the plume front and tail support better understanding of field situations. A user-ready spreadsheet is provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".