Bibliographic record
Abstract
Introduction Water plays a key role in the transfer of mass and energy within the Earth system. Incoming solar radiation drives evaporation of about 434 000 km 3 a –1 from the ocean surface and 71 000 km 3 a –1 from the land surface, while precipitation delivers about 398 000 km 3 a –1 of water to the ocean and 107 000 km 3 a –1 to the land surface. The balance is redressed through the flow of 36 000 km 3 a –1 of water from the land to the oceans via rivers (data in Berner and Berner, 1996). Environmental change affecting any of these water transfers produces changes in runoff and river flows, hence in the rivers themselves. Changing climate is intensifying the global hydrological cycle, leading to significant changes in precipitation, runoff and evapotranspiration (Huntington, 2006; Bates et al ., 2008; see also Chapter 1). Intensification of the hydrological cycle is likely to mean an increase in hydrological extremes (IPCC, 2001). Changes in the frequency distribution of precipitation alter water flows and water availability in the surface environment leading, in turn, to a change in river regimes. These factors are superimposed upon the effects of human actions associated with land use and with the attempt to control water for various uses that have directly changed river channels and the quality of water flowing in them. Land surface condition mediates quantity of water and the amount and calibre of sediment delivered to rivers which, in turn, influences river sedimentation, morphology and stability. Humans also manipulate the terrestrial hydrological cycle deliberately by construction of reservoirs, abstractions of water for human use, and discharges of water into river courses. Moreover, we directly modify watercourses by realigning them, by river ‘training’ works, by dredging, by fixing banks and building dykes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.094 | 0.039 |
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".