Bibliographic record
Abstract
Abstract. This paper briefly reviews the contemporary issues of Water Security, noting that current and prospective pressures represent major challenges for society. It is argued that, given the complex interdependencies and multi-faceted nature of these challenges, new trans-disciplinary science is needed to support the development of science-based policy and management. The effects of human society on land and water are now large and extensive. Hence we conclude that: (a) the management of water involves the management of a complex human-natural system, and (b) potential impacts of the human footprint on land and water systems can influence not only water quantity and quality, but also local and regional climate. We note, however, that research to quantify impacts of human activities is, in many respects, in its infancy. The development of the science base requires a trans-disciplinary place-based focus that must include the natural sciences, social sciences and engineering, and address management challenges at scales that range from local to large river basin scale, and may include trans-boundary issues. Large basin scale studies can provide the focus to address these science and management challenges, including the feedbacks associated with man’s impact from land and water management on regional climate systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".