АВТОНОМНЫЙ ЭНЕРГОТЕХНОЛОГИЧЕСКИЙ КОМПЛЕКС ДЛЯ ПОЛУЧЕНИЯ ВОДОРОДА В КАЧЕСТВЕ ВТОРИЧНОГО ЭНЕРГОНОСИТЕЛЯ
Bibliographic record
Abstract
The energy-technological complex (ETC) destination is the transforming of primary sun/wind energy into electric one as well the sub-products fabrication. The ETC consists of the following constituent elements that are to be characterized by the harmonized parameters: wind power station, photo-voltaic transformer, distiller, fuel cell (produced by Astris Energy Inc., Canada), hydrogen and oxygen generator like the electrolyzer and compressed gases storing and supply system (SSS). The hydrogen and oxygen are generated in the electrolyzer and stored in the SSS and then used in fuel cell for convention electric energy generation. The desalination of seawater and sea salt yielding is the ETC output as well. The base ETC configuration with power 2 x 3 = 6 kW are considered. The operational peculiarities of ETC constituent element are considered as well. The alternative possibility of hydrogen accumulation by the carbone nanostructures (produced by CARBOLEX, and ALPHA AESAR) was considered. The creation and operational demonstration of the autonomous ETC are supported by STCU (project #UZB-23j) and with collaboration with Nano-Technology Institute at Texas University and HONEYWELL.
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 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".