Influence of hydrological connectivity on winter limnology in floodplain lakes of the Saskatchewan River Delta, Saskatchewan
Bibliographic record
Abstract
Globally, hydrological connectivity between rivers and their floodplains has been reduced by river flow management and land transformation. The Saskatchewan River Delta is North America’s largest inland delta and a hub for fish and fur production. To determine the influence of connectivity on limnology within this northern floodplain, water chemistry and stable isotopes (δ18O and δ2H) were analyzed during the winter of 2014 in 26 shallow lakes along a hydrological gradient. A total of five lake connectivity categories were determined by optical remote sensing imagary of surface water coverage area from years of varying flood intensities. Accuracy of categories was verified by degree of 18O and 2H enrichment within lakes. Both isotopes showed marked successional enrichment between connectivity categories, with more isolated lakes exhibiting greater enrichment. Water chemistry in lakes with greater connectivity to the main channel were characterized by higher pH, dissolved oxygen, nitrates, and sulfates and lower total nitrogen, total phosphorus, and ammonium compared with more isolated lakes. These findings illustrate how connectivity influences water chemistry in northern floodplain lakes and how it might determine the suitability of these lakes as winter refuge for fishes. Additionally, our study provides supporting evidence for the effective use of optical remote sensing imagery, an inexpensive and accessible source of data for researchers, when determining connectivity characteristics of large northern floodplain systems. Additionally, this study provides further evidence that the inundation of floodplain lakes by river water during peak discharge has an impact on the conditions within the lakes long into the winter ice-cover season. Understanding the year-round influence of river–floodplain connection is imperative for assessing potential impacts of climate change and future water regulation on such ecosystems.
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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".