Canadian Arctic Archipelago Rivers Project Geochemical Data 2014-2016
Bibliographic record
Abstract
This data set describes geochemical samples collected from 25 rivers and 11 lakes throughout the Canadian Arctic Archipelago (CAA). CAA rivers were sampled as part of the Canadian Arctic Archipelago Rivers Program (CAA-RP) and the Canadian Arctic GEOTRACES program with access via land, water, and air during the summer seasons of August 1-September 9, 2014, and August 11-19, 2015. Time series observations were also collected from the Coppermine River in Kugluktuk, Nunavut (NU) (year-round; August 5, 2014 to August 23, 2016), and from Freshwater Creek in Cambridge Bay, NU (open water only; June 19, 2014 to September 16, 2016). Lake samples were collected opportunistically during float plane air-surveys in 2014 and 2015 as part of the CAA-RP study in the southern CAA. Precipitation was collected during two significant rain events in Kugluktuk, NU (August 25, 2015) and Cambridge Bay, NU (August 20, 2016). River water samples were collected according to methods developed by the Arctic Great Rivers Observatory (Arctic-GRO; http://www.arcticgreatrivers.org/); lake sampling followed the same general methods, with collection carried out in deeper waters away from the shore; rain samples were collected using an HCl cleaned plastic box and processed immediately the morning following the rain event in order to limit the influences of evaporation. Sampling and analytical methods are described in detail in Brown et al., 2020.This data set includes the raw data supplied in Supplementary Tables S2 (Geochemical Data for the CAA-Rivers Project, collected from 2014 - 2016) and S3 (Geochemical Time Series Data for the Coppermine River and Freshwater Creek collected from 2014 - 2016) that accompany Brown et al., 2020. In addition to geochemical observations, the following calculated parameters can be found in Supplementary Table S2 of Brown et al., 2020: drainage basin area; predominant bedrock lithology; percent coverage of lakes; and surficial geology characteristics. These parameters were determined for each river drainage basin as described in the text and associated references found in Brown et al., 2020.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".