Investigating the applicability of end‐member mixing analysis (EMMA) across scale: A study of eight small, nested catchments in a temperate forested watershed
Bibliographic record
Abstract
Current interest in multicatchment hydrologic studies challenges the use of geochemical mixing models across scale, where changes in stream water chemistry from catchment to catchment may indicate (1) changes in the proportional contributions of end‐members, (2) changes in the geochemical signatures of end‐members in space, or (3) changes in the geochemical signatures of end‐members in time. In this study we examine stream water chemistry from a series of eight nested catchments in a 1.47 km2 temperate forest watershed in southern Quebec for evidence of contributing end‐members. We use eigenvector and residual analysis (Hooper, 2003) of the multivariate stream water chemistry records to estimate the dimensionality of the mixing space for each individual catchment, indicating the number of contributing end‐members. Using the mixing space of the largest, highest‐order catchment (1.47 km2), we evaluate its ability to predict stream water chemistry in the seven upstream catchments, representing progressively smaller areas. We observe significant spatial variation in ionic mixing ratios within the 147 ha watershed. Only spatial testing across catchments allowed us to identify appropriate conservative tracers most compatible with the application of a single mixing model across scale. On the seasonal timescale, groundwater geochemistry changes significantly due to the recharge from spring snowmelt, indicating a mixture of two groundwater end‐members of varying age. On the timescale of storm events, shallow perched water and throughfall provide geochemical signatures consistent with physical mixing while unsaturated zone soil water sampled from local pockets of glacial till does not. Our results suggest cautious application of end‐member mixing analysis (EMMA) for multicatchment studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".