Source-to-Stream Connectivity Assessment Through End-member Mixing Analysis
Bibliographic record
Abstract
Summary Streamflow sources across various hydrologic conditions were examined in a 5.1 ha temperate humid forested catchment (Laurentians, Canada). In that system, the relationship between rainfall and runoff is nonlinear, thus hinting towards complex processes involving critical, transient source areas and a changing catchment internal state of connectivity. Multiyear daily stream chemistry data were broken down into several hydrologic scenarios reflecting different conditions with respect to stream discharge and antecedent catchment wetness. End-member mixing analysis and mass balance calculations were performed to: (1) compare the dimensionality of the mixing spaces (i.e. the number of streamflow sources) obtained under 64 different hydrologic scenarios; (2) screen independently sampled end-members (i.e. the nature of sources) to assess catchment connectivity from a spatial perspective; and (3) estimate the relative contributions of end-members to streamflow to characterize hydrological connectivity from a volumetric standpoint. Mixing space dimensionality did not vary significantly among the tested hydrologic scenarios, as three end-members were generally required to account for most of the variance in stream geochemistry. Differences were significant in the ability of the tested end-members to fit in mixing spaces; for instance, throughfall and organic soil water end-members better fitted in mixing spaces associated with high rather than low discharges. The relative contributions of end-members to streamflow were highly variable in time. Scenarios involving low discharges and dry antecedent conditions were mostly associated with baseflow, while scenarios involving high discharges and wet antecedent conditions were associated with increased proportions of throughfall and organic soil water from downstream downslope and downstream upslope areas. These results suggest a cautious evaluation of the predictive power of one single mixing space with regards to the nature of streamflow sources across hydrologic conditions.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".