Physical and chemical characteristics of 1300 lakes and ponds across the Canadian Arctic
Bibliographic record
Abstract
Lakes and ponds are a major feature of the Arctic landscape and are recognized as effective ‘sentinels of change’. Here we present water chemistry characteristics of lakes and ponds (n=1300 with 26 variables) across the Canadian Arctic collated from published studies. We also extracted geological and ecoregion data in an attempt to determine the key drivers. In general, most lakes were shallow (85.4%, <10 m), nutrient (phosphorus) poor (oligotrophic = 45.6% and ultra-oligotrophic = 24.8%), located at low elevation (66.5%, <200 m asl), close to coastlines (72.5%, 0-50 km), and underlain by sedimentary geology (66.5%). The first two components from Principal Component Analysis explained 49.3% of the variation in the dataset; the first component was dominated by conductivity/carbonate materials, and the second component suggested allochthonous inputs of phosphorus. In general, bedrock geology is the primary driver of water chemistry; as such, there were major differences between lakes underlain by igneous and sedimentary rocks. Those on sedimentary bedrock tend to have higher pH, nutrients and higher inorganic ion concentrations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".