Stable isotope data (oxygen-18 and deuterium) from surveys of lakes, wetlands, rivers, and input waters across the South Athabasca Oil Sands region, Alberta, 2007–2009
Bibliographic record
Abstract
Oxygen-18 and deuterium analyses of water samples are provided from a regional survey of lakes, wetlands, soil waters, groundwaters, and snowpack samples collected in the Southern Athabasca Oil Sands (SAOS) region, Alberta, Canada, mainly during 2007-2009. Lake, wetland, and river sampling were conducted by helicopter during late summer, capturing conditions close to peak evaporative enrichment. Shallow soil water from the unsaturated zone was also collected in late summer, whereas deeper groundwaters from Quaternary aquifers, Quaternary channels, and uppermost Cretaceous strata, were collected primarily as part of winter drilling programs by industrial partners. Snowpack samples were collected in late March/early April, prior to significant spring melt. This dataset includes 1576 isotopic analyses made on 788 water samples as well as selected isotope mass balance model outputs (lake evaporation/inflow and water yield to lakes). These basic model data are provided to facilitate evaluation of the method as a tool for spatial mapping of water yield and its interannual variability. Details and further discussion on the isotope mass balance approach are provided in "Mapping water yield distribution across the southern Athabasca Oil Sands area: baseline surveys applying isotope mass balance of lakes" (Gibson et al., 2019). 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) | 0.001 | 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) | 0.003 | 0.001 |
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".