INCOSE Practitioners Challenge 2019: Clean Water and Sanitation in the Ganges River Basin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT During the INCOSE International Symposium 2019, INCOSE issued a Practitioners Challenge to address the problem of clean water and sanitation in the river Ganges basin in support of the broad focus INCOSE has placed on the topic of clean water and sanitation to tackle the National Academy of Engineering (NAE) Grand Challenges previously identified by the INCOSE Academic Council. The INCOSE Board chose to continue the Academic Council's work on the NAE Grand Challenges, focusing on Clean Water and Sanitation (CWS) and working to establish Memoranda of Understanding (MOUs) with organizations to provide systems expertise as appropriate. The United Nations’ (UN) may be one such organization with their focus on global Clean Water and Sanitation in their Sustainable Development Goal 6 (SDG 6). The team was asked to demonstrate the application of Systems Engineering principles and methods to explore solutions to achieve clean water for the inhabitants of the Ganges River basin. After applying different systems engineering techniques and carrying out research, the team identified a multi‐facetted approach to addressing the clean water challenge, identifying key areas where systems engineering can be of benefit. Though this problem is, on the surface, one of technology and land use, it is set against the backdrop of arguably one of the most complex socio‐economic regions on the earth. The need to address cultural aspects of the system and facilitate changes in human behavior, therefore, stands out as being particularly important in order to affect a successful outcome. Another key observation was that approaching and achieving the UN goals individually could lead to undesirable, unintended consequences due to strong interdependencies. This is an area where systems engineering could make a major contribution.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it