Northwest Passage Project seawater dataset, July - August 2019, Canadian Arctic Archipelago
Bibliographic record
Abstract
This data were collected and processed through the Northwest Passage Project, a US National Science Foundation funded program to explore the changing Arctic through an innovative expedition that engaged diverse audiences through real time interactions from sea, a high definition 2-hour documentary, and related community events. The expedition was conducted onboard the Research Vessel Ice Breaker (RVIB) Oden, where undergraduate and graduate students participated in the expedition along with local indigenous representatives, scientists, historians, journalists, and a documentary film crew. We are submitting two seawater datasets: one with in-situ and ex-situ data, and one with the waters composition results. The "NPP.full.dataset.csv" provides information on the following parameters collected from the Canadian Arctic Archipelago water column: Temperature, Salinity, Turbidity, Oxygen isotopes, Nutrients, Particulate Organic Matter (POM) data (e.g., Particulate Organic Carbon (POC), Particulate Organic Nitrogen (PON)), Methane data (e.g., concentrations, isotope ratio C13-CH4, oxidation rates constants). The "NPP.OMP.dataset.csv" provides information on the water mass contribution across the Canadian Arctic Archipelago. Here, we show the raw and standardized parameters used into the Optimal MultiParameter analysis (OMP) (such as, Absolute Salinity, the Arctic Nitrate:Phosphate tracer, and d18O), and the source waters fractions (i.e., Atlantic Water, Pacific Water, Meteoric Water, Sea Ice Meltwater). We also show the residuals of the OMP analysis in fractions. Both the datasets provide unique information on physical and chemical characteristics of the Canadian Arctic Archipelago waters, which will serve as baseline for further investigations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.017 |
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".