Isotopic and geochemical surveys of lakes in coastal B.C.: Insights into regional water balance and water quality controls
Bibliographic record
Abstract
This study was conducted within a 100,000 km2 area of British Columbia, (B.C.) Canada including Vancouver Island, the Georgia Basin, and the Pacific and Kitimat mountain ranges rising from the Pacific Ocean. A stable isotope mass balance method is applied to estimate evaporation loss and water yield from a remote network of 560 lakes on Vancouver Island and coastal B.C., based on helicopter sampling surveys conducted between 2008 and 2015. Spatial patterns in derived hydrological parameters are compared to water quality indicators and watershed characteristics to provide insight into water quantity and water quality relationships in the region, to be incorporated within a future critical loads assessment. Regional trends in lake water balance, underlying physical drivers, and geochemical processes potentially influencing critical loads of acidity are described. Dominant non-anthropogenic regional drivers of geochemistry include sea spray, lithology, weathering and elevation. Significant contrast is noted in alkalinity between the sedimentary and volcanic substrates on Vancouver Island and igneous intrusive substrates of the Pacific and Kitimat ranges. A positive correlation is found between elevation and water yield to lakes, while the opposite is observed for rivers, which is interpreted to reflect disconnection of low elevation lakes from regional drainage networks. This may invalidate use of river gauge data for critical loads assessment in this or similar regions.
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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 | 0.000 |
| 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".