The intrinsic value of water: a proposal
Bibliographic record
Abstract
This paper returns to the controversy about the Intrinsic Value of Water (IVW) which should be included in the price paid by the consumer. The IVW is calculated based on the solar energy involved in the water cycle, which annually generates a considerable volume of water of the highest quality without human intervention. In 2014, Mexico invested a budget of 3,700 million USD in water management. This came mainly from government subsidies, with a quarter coming from water rates. The volume of water available is 459 km3, approximately 16.5 % of which comes under the field of federal administration, including the volume of dams and the concession of rivers and groundwater. The federal budget divided by the volume of managed water is the water cost (0.043 USD m-3). The federal agency delivers the water in bulk to the states and the municipalities through the Watershed Councils, who determine the price of water to users based on their availability nationwide. The energy required to evaporate the water of the annual precipitation in Mexico is 1.01x1012 kW h year-1. This energy is divided by the precipitate volume and then multiplied by the lower price of kW h to calculate the intrinsic value of water: 0.036 to 0.13 USD m-3. The federal, municipal and intrinsic costs are added to calculate the final value. The IVW is a kind of natural goodwill that each country should pay according to their hydrological balance.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".