Land cover controls on depression‐focused recharge on the Oak Ridges Moraine, southern Ontario, Canada
Bibliographic record
Abstract
Abstract The Oak Ridges Moraine (ORM) is a key hydrogeologic feature in southern Ontario. Previous research has emphasized the importance of depression‐focused recharge (DFR) for the timing and location of water recharge to the ORM's aquifers. However, the significance of DFR has not been empirically demonstrated, and the ORM's permeable surficial deposits imply that rainfall and snowmelt will largely recharge vertically rather than move laterally to topographic depressions. The exception may be during winter and spring, when concrete soil frost limits infiltration and encourages overland flow. The potential for DFR was examined for closed depressions under forest and agricultural land covers with similar soils and surficial geology. Air temperatures, precipitation, snow depth and water equivalent, soil water contents, soil freezing, and depression surface‐water levels were monitored during the winter and spring of 2012–2013 and 2013–2014. Recharge (R) was estimated at the crest and base of each depression using a 1‐dimensional water balance approach and surface‐applied Br−tracing. Both forest and agricultural land covers experienced soil freezing; however, forest soils did not develop concrete frost. Conversely, agricultural fields saw concrete frost, overland flow, episodic ponding, and subsequent drainage of rain‐on‐snow and snowmelt inputs in open depressions. Recharge at the base of open depressions exceeded that in surrounding areas by an order of magnitude, suggesting that DFR is a significant hydrologic process during winter and spring under agricultural land cover on the ORM. Closed topographic depressions under agricultural land cover on the ORM crest may serve as critical recharge “hot spots” during winter and spring, and the ability of the unsaturated zone beneath these depressions to modify the chemistry of recharging water deserves further attention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".