Assessment heat impact of the Belarusian nuclear power plant on environment
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
United Kingdom (132.8MW).Other leaders included Italy (32.7 MW), Germany (24 MW), Ukraine (14.6 MW) and Canada (13.1 MW).It's a clean source of renewable energy that produces no air or water pollution.And since the wind is free, operational costs are nearly zero once a turbine is erected.There are three regions with the largest potential to produce electricity from wind turbines in Belarus: Grodno, Minsk and Mogilev regions with average wind speed of 5.5-6.5 m/s near the ground and 6.5-7.5 m/s at the height of 40 m..At the moment 56 windmills are installed in Grodno, Minsk, Vitebsk and Mogilev Regions (total capacity -43.2 MW).The first wind park in Belarus with capacity of 9,0 MW was installed in Grabniki (Grodno region) this year.It includes 6 power units (China production) with capacity of each -1.5 MW.The height of tower each unit is 90 m, blade length -40 m, annual average electricity production is about of 84 GW.Doubtless, we see that this trend is promising enough, but some problems exist too, which hampering of wind energy development in Belarus.Main of this problems are high investment cost and absence of national producers of wind power units, low level of feed-in tariffs for wind energy (1,2 at present); absence of wind speed measurement on the wind turbines placement (70-100 m) and others.So this work is dedicated to the analysis of current state of wind energy in the world and Belarus and the discussing of above mentioned problems.
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.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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 it