ROBIN: Reference observatory of basins for international hydrological climate change detection
Bibliographic record
Abstract
Human-induced warming is modifying the water cycle. Adaptation to posed threats requires an understanding of hydrological responses to climate variability. Whilst these can be computationally modelled, observed streamflow data is essential for constraining models, and understanding and quantifying emerging trends in the water cycle. To date, the identification of such trends at the global scale has been hindered by data limitations - in particular, the prevalence of direct human influences on streamflow which can obscure climate-driven variability. By removing these influences, trends in streamflow data can be more confidently attributed to climate variability. Here we describe the Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) - the first iteration of a global network of streamflow data from national reference hydrological networks (RHNs) - comprised of catchments which are near-natural or have limited human influences. This collaboration has established a freely available global RHN dataset of over 3,000 catchments and code libraries, which can be used to underpin new science endeavours and advance change detection studies to support international climate policy and adaptation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".