Bibliographic record
Abstract
During the last four decades a large number of dams have been constructed in our country for water resources. The water stored in the reservoirs so created is being utilized mainly for irrigation, generation of hydropower, industrial use and drinking water purpose. The number of large dams (more than 15 m height) as per ICOLD classification in India are more than 3820 Nos. There are more than 40,000 large dams in the world. Obviously, the safety of such large number of large dams is of public concern. Necessary organizations are established to monitor the safety concerns of such dams in different states. Accordingly, Dam Safety Organization (DSO) for Gujarat State, was started in July 1981 under Central Designs Organization with it's head quarter at Gandhinagar. Subsequently, this organization was brought under the control of Chief Engineer and Director, Gujarat Engineering Research Institute (GERI) since October, 1986 with it's head quarter at Vadodara. This organization is monitoring safety aspects of the dams in the Gujarat State and works in co-operation with Dam Safety Monitoring Directorate, Central Water Commission at New Delhi. Each dam is said to be an Ambassador of the Engineering profession and the dam engineers should be aware of all the tools available for the safety evaluation of each dam. Whenever deficiencies are observed during inspection of the dam, timely remedial measures are required to be taken for the safety of the dam for bringing the dam to a safe stage.
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 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".