Connections between River Runoff and Limnological Conditions in Adjacent High Arctic Lakes: Cape Bounty, Melville Island, Nunavut
Bibliographic record
Abstract
Hydrological and hydrochemical monitoring of paired watersheds in the High Arctic was conducted in 2003–04 to investigate the influence of seasonal runoff on lake water chemistry and productivity. Despite similar limnological conditions overall between the two lakes, marked differences in aquatic productivity were attributed to watershed and basin morphology and the resultant influences on lake ice deterioration and growing season length. A switch from allochthonous to autochthonous sources of carbon late in the season reflected the simultaneous decline in river runoff and increase in aquatic productivity as the growing season progressed. However, low air temperatures and protracted snowmelt and ponding in the deeply incised channel of one river in 2003 led to greater solute accumulation in runoff that was discernable in hydrochemical profiles of that lake, even though runoff was greater in 2004. Notwithstanding, calculated nutrient fluxes were greater during the higher-flow year (2004), but mixing was impeded by underflow conditions in the lakes. Despite these differences, connections between river and lake water chemistry appeared weak even with marked seasonal changes in the volume of runoff. Our results highlight the interconnection between site-specific features and hydroclimatic factors like snowmelt and lake ice conditions in influencing limnological conditions and suggest that similar systems may respond differently to the same hydroclimatic 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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".