Synoptic sampling reveals spatial stability in flow and chemistry patterns despite large seasonal variability in a subarctic mountain catchment
Bibliographic record
Abstract
Abstract Seasonality plays a critical role in cold mountain regions as variation in air temperature, ground thermal status, and precipitation phase alter the rate, timing and magnitude of hydrological and chemical transport. Additionally, cold mountain catchments can have highly variable topography, geology, permafrost, and landcover, which intrinsically add to this irregularity. Understanding how external and internal variability act to control mass fluxes requires sampling at a high spatial resolution over time, which rarely occurs in cold remote regions. In this work, we conduct five snapshot sampling surveys across 34 subcatchments during the ice‐free period in Wolf Creek Research Basin (a mesoscale montane subarctic catchment) and two additional winter surveys across a subset of sites to assess the drivers of variability in stream chemistry and discharge. We sampled for specific conductance (SpC), major ions, and dissolved organic carbon (DOC) and used statistical metrics and Bayesian mixing analysis to quantify patterns of flow and chemistry across space and time. Our results indicate patterns in both flow and chemistry remain largely consistent across seasons for all solutes. However, there was weaker correlation of chemistry between sites, suggesting asynchronous behaviour within the catchment. There was evidence of increasing production of ions and DOC along the stream network during high spring flows but not during low flows. Although concentrations and flows exhibit high seasonality in subarctic mountains, this seasonal variability does not alter spatial patterns that arise from highly variable catchment characteristics.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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 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".