Spatial and temporal patterns of redox-sensitive and bio-relevant micronutrients in a large complex binational lake system: Lake of the Woods
Bibliographic record
Abstract
We analyzed lower water column redox-sensitive and bio-relevant (Al, Fe, Mn) micronutrient data between 2018 and 2019 to assess spatial and seasonal variability within Lake of the Woods (LoW) as they relate to continued eutrophication and water quality issues. The sediment–water interface represents a dynamic linkage between the sediment and overlying water column, dominated by diffusive flux and particulate resuspension. The complexity of LoW basins, shoreline extent, and underlying geology (situated on both the Canadian Shield and Glacial Lake Agassiz Lakebed sediments) creates variable water quality conditions, with cyanobacterial and harmful algal blooms (cHABs) particularly prevalent in the southern basins. We used a multivariate approach to differentiate basins within LoW, to better understand potential mechanisms underlying eutrophication and cHAB development. A unique signature of redox-sensitive metals was observed above the lakebed suggesting differential impacts of diffusive flux and resuspension dynamics, with significantly different micronutrient signatures between locations on vs off the Canadian Shield. The basins on the Glacial Lake Agassiz lakebed including Big Traverse, Little Traverse, Morson and Bishop were statistically comparable (p > 0.05), and significantly different from those on the Canadian Shield (p < 0.05). Correlations between bio-relevant element vanadium (element potentially used in nitrogen fixation) and chlorophyll-a), suggest potential elemental drivers of cyanobacterial ecology in LoW. An integration of multi-disciplinary research may serve to better address water quality issues as they relate to underlying geology and improve monitoring programs into the future to further delineate the physical-biogeochemical processes differentially impacting basins within LoW.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".