Impact of Small, Canadian Arctic River Flows (SCARFS) to the Freshwater Budget of the Canadian Archipelago, 2014 - 2016
Bibliographic record
Abstract
This three-year study proposes to characterize the geochemistry of the largely unstudied, remote rivers and estuaries in the Canadian Arctic Archipelago (CAA), with the ultimate aim of resolving the contribution of local freshwater inputs to CAA boundary currents. The investigators will target seven rivers for sampling. River sampling will occur during different flow regimes, but will emphasize sample collection during the spring freshet, the time of year when terrestrial runoff from local CAA rivers maximally impacts coastal waters. Estuarine sampling will include both horizontal and vertical profiles. The data will be synthesized and interpreted using mixing models and regression analyses, and compared with data for the Mackenzie and Yukon Rivers, which have historically been considered "typical" for North American runoff to the Arctic. Acceleration of glacial melt and the hydrologic cycle in a warmer world will likely increase local inputs of freshwater to the CAA. Because many such changes may already be underway, it is important to establish a baseline by which future changes can be compared. The proposed research has implications regarding the variability and magnitude of freshwater export from the Arctic and subarctic oceans to the North Atlantic, and the findings will help fill significant gaps in the knowledge of CAA river geochemistry and the Arctic freshwater cycle. The project will catalyze a new partnership among faculty, researchers, and students at the Applied Physics Laboratory and Northwestern University. Moreover, the research will include hands-on participation from local community members who will collect samples during various times of year to extend the temporal coverage of the study. Data will be made publicly available via websites and databases for maximum visibility, dispersion, and long-term archival. Additional activities include teaching and training of a postdoctoral student, incorporation of project findings into curricular materials, and outreach to K-12 students from traditionally underrepresented groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".