Comment on essd-2022-306
Bibliographic record
Abstract
Abstract. The Canadian Arctic Archipelago (CAA) acts as a watershed discharge in the Arctic Ocean, as it is characterized by advection from the Pacific and Atlantic waters, ice melt, local river discharge and net precipitation. Its waters are characterized by the mixing of Pacific and Atlantic water origin, and the meltwater supply originating from the Devon Ice Cap Glaciers and marine-terminating rivers. The Special Report on the Ocean and Cryosphere in a Changing Climate published by the IPCC in 2021, showed how the runoff into the Arctic Ocean increased for Eurasian and North American rivers by 3.3 ± 1.6 % and 2.0 ± 1.8 % respectively (1976–2017), hence, monitoring the freshwater supply within the CAA is crucial in a warming scenario. This paper aims to describe the water mass structures within the CAA, by analyzing physical and chemical tracers collected in 2019 during the Northwest Passage expedition held in July and August onboard the Swedish icebreaker Oden. The uniqueness of this study is the wide dataset composed of physical and chemical parameters (https://doi.org/10.18739/A2W66995R). Here, we implemented the Optimal Multiparameter analysis for the detection of the source water fractions, such as, Atlantic Water (AW), Pacific Water (PW), Meteoric Water (MW), and Sea Ice Meltwater (SIM). For this analysis, we used a nutrient ratio tracer defined Arctic Nitrate-Phosphate tracer, together with the absolute salinity and δ18O from the water samples. Our analysis confirmed the intrusion of the PW from the west in the upper layers and of AW from the east in the deeper layers. We also discriminated the meltwaters between glacial and sea ice origin and showed their spatial distribution in the study area. This study provides unique set of data from this under observed region and can serve as baseline for further analysis and continued data collection.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.570 | 0.426 |
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".