Scale and watershed features determine lake chemistry patterns across physiographic regions in the far north of Ontario, Canada
Bibliographic record
Abstract
Changes in the far north of Ontario (>50°N latitude), like climate warming and increased industrial development, will have direct effects on watershed characteristics and lakes. To better understand the nature of remote northern lakes that span the Canadian Shield and Hudson Bay Lowlands, and to address the pressing need for limnological data for this vast, little-studied area of Ontario, lake chemistry surveys were conducted during 2011-2012. Lakes at the transition between these physiographic regions displayed highly variable water chemistry, reflecting the peatland landscape with a mix of bog and fen watersheds, and variations in the extent of permafrost. In the transition area, Shield and Lowlands lakes could not be clearly differentiated based on water chemistry; peat cover decouples, to varying degrees, the lakes from the influences of bedrock and surficial deposits. Regional chemistry differences were apparent across a much broader area of northern Ontario, due to large-scale spatial changes in geology and in the extent of peatlands and permafrost. Shield lakes in the far northwest of Ontario had Ca, Mg, and TP concentrations markedly higher than those of many Lowlands lakes and previously studied Shield lakes south of 50°N, related to an abundance of lacustrine and glacial end-moraine deposits in the north.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".