A Machine Learning-Reconstructed Dataset of River Discharge, Temperature, and Heat Flux into the Arctic Ocean
Bibliographic record
Abstract
Arctic rivers deliver 11% of the global river discharge volume into the Arctic Ocean, influencing ocean circulation, sea ice, and coastal ecosystems. Our understanding of these patterns is limited by substantial data gaps. To address this, we present the Reconstructed Arctic-draining river DIscharge and Temperature (RADIT) dataset, a comprehensive record of reconstructed daily discharge, temperature, and heat flux for 25 major Arctic rivers from 1950 to 2023. Based on machine learning regression methods and ERA5-Land reanalysis data, we designed distinct reconstruction frameworks for discharge and temperature, considering the different characteristics of the observational data. We achieved high reconstruction accuracy, with median Nash-Sutcliffe efficiency (NSE) values of 0.861 for discharge and 0.906 for temperature. The RADIT dataset, with extensive spatial and temporal coverage, is a valuable resource for understanding Arctic hydrology and its response to climate change. It will improve Arctic freshwater budget quantification, climate model calibrations, and assessments of river impacts on the Arctic Ocean, enhancing our understanding of the role of the Arctic Ocean in the global climate system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".