Water‐IQ matters as water conflicts mount
Bibliographic record
Abstract
Abstract Water crises fuel conflicts that confound efforts to solve the underlying water crises. Water diplomacy is more effective at defusing such conflicts when the parties involved share at least a common understanding of the water involved. We argue that basic, but still up to date knowledge of where water is and how it moves is so important for finding common ground in water conflicts that this knowledge deserves a name of its own—the Water Intelligence Quotient or Water‐IQ. Science has advanced, and what people learn about the water cycle needs to reflect that. Two keystones of Water‐IQ are awareness of how profoundly people have influenced the water cycle and the atmospheric teleconnections that move water between geographic regions. Given the importance of evidence‐based knowledge of the water cycle when trying to overcome water conflicts and seek a basis for water cooperation, Water‐IQ knowledge needs to be spread widely. This article is categorized under: Human Water > Water Governance Water and Life > Conservation, Management, and Awareness Human Water > Water as Imagined and Represented
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".