Watershed, climate, and stable isotope data (oxygen-18 and deuterium) for 50 boreal lakes in the oil sands region, northeastern Alberta, Canada, 2002–2017
Bibliographic record
Abstract
Watershed data, climate and stable data collected over a 16-year period from a network of 50 lakes in northeastern Alberta, are provided to allow for broader incorporation into regional assessments of environmental impacts, particularly hydrologic and geochemical processes under changing climate and land use development. Oxygen-18 and deuterium analyses of water samples are provided from late summer surveys of 50 lakes with varying land cover and permafrost conditions. Six sub-groups of lakes are represented, including Stony Mountains, West Fort McMurray, Northeast Fort McMurray, Birch Mountains, Caribou Mountains and Shield. This dataset includes 1582 isotopic analyses made on 791 water samples and 3164 isotope mass balance model outputs, as well as 800 lake/watershed parameters, 5600 climate parameters, and 800 modelled values for isotopic composition of precipitation used in the computations. Model data are provided to facilitate evaluation of transferability of the model for other applications, and to permit more sophisticated spatial analysis and intercomparison with geochemical and biological datasets. Details and further discussion on the isotope mass balance approach are provided in "Regional trends in water balance and runoff to fifty boreal lakes: a 16-year isotope mass balance assessment including evaluation of hydrologic drivers" [1]. Overall, the data are expected to be useful, in comparison with local and regional datasets, for water resource management and planning, including design of monitoring networks and environmental impact assessments for oil sands projects.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".