Tidal Range Energy Resource Estimation of Khor Kalmat using Geostatistical Modeling
Bibliographic record
Abstract
Electrical power generation by tidal energy provides various advantages. The energy is highly predictable, has less impact on ecological pollution and provides an indefinite amount of renewable energy. The countries like Canada, China, Russia, South Korea and France are extensively utilizing tidal sources of energy for the generation of electrical energy. A suitable site (where less construction is required), adequate tidal range and sufficient bathymetry; are the basic requirements for the installation of a tidal power plant however sometimes there is no tidal data available for suitable sites, like Khor Kalmat tidal lagoon in Pakistan. Therefore, the first time study is conducted to assess the tidal energy resources of the naturally blessed lagoon, Khor Kalmat, which is located in the Baluchistan province of Pakistan, by using geostatistical modeling. A geostatistical model is developed to estimate the tidal energy potential at Khor Kalmat by using observed data of five available locations along with the coastal belt of Pakistan. Models are designed by integrating several layers into ArcGIS. These layers include tidal data, satellite metaphors and other physical and socioeconomic layers. After processing of data, digitized models and layers are generated. Five different models have been compared and the best model is carefully chosen to predict the tidal data of Khor Kalmat after validation of the individual model, During the study, it was observed that low head hydro tidal turbine of Venturi-Enhanced Turbine Technology (VETT) is best suited for harnessing tidal energy due to adequate tidal range. Consequently, by means of a bi-directional VETT device, the output power is assessed to be 269.93 MW.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".