Calculating the cost of irrigation induced soil salinization in the tungabhadra project
Bibliographic record
Abstract
Irrigation projects in developing countries have a history of poor performance. Inefficiencies result as water applications deviate from plans and induce greater than projected rates of soil degradation through water logging and salt accumulation. Over time, the collective impact of these forces will converge to an equilibrium with a level of output that may be far below the system’s potential. The Tungabhadra Project in south west India is experiencing all of these problems. Integrating geographic, hydrologic, biologic and economic features, the lost production value is estimated for a range of equilibria to which this system may converge. For the lower left bank main canal of the Tungabhadra project, the total economic cost of soil degradation are approximately 14.5% of the system’s productive potential while sub-optimal distribution losses may approach 37.1%.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Economic costing of irrigation induced soil salinization; a domain question in agricultural economics.
The study estimates economic costs of soil salinization in an irrigation project.
Economic analysis of irrigation-induced soil salinization in India; domain agricultural economics, not metaresearch.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".