The influence of seasonal temperature variation and other factors on the occurrence of dissolved manganese during river bank filtration
Bibliographic record
Abstract
The water quality from production wells used by the City of Fredericton is generally good, with the exception of elevated dissolved Mn. This occurrence has been attributed to the flux of dissolved organic carbon (DOC) that enters the aquifer during river water infiltration, and the subsequent creation of in situ conditions suitable for reductive dissolution of Mn-oxides. A geochemical investigation near a well that induces river water infiltration indicates a potential seasonal dependence of the redox conditions that may be governed by water temperature, although elevated Mn persists at depth and adjacent to the production well. It has also been speculated that Mn-oxide mineral depletion will eventually occur in the aquifer sediments, and that this occurrence could be followed by elevated dissolved Fe in the extracted water. In this contribution, reactive transport modelling is applied to assess the impact of seasonally-varying river water temperature on dissolved Mn, and to investigate the factors controlling Mn-oxide depletion. Employing an empirical function for temperature-dependent reaction rate, and using the measured time series for river water temperature, produces simulated spatial and temporal patterns of dissolved Mn that are similar to the observed data. The long-term persistence of elevated Mn is shown to be strongly controlled by the travel time from the river to the well, and by the initial pyrolusite content of the aquifer sediments.
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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) | 0.000 | 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".