H₂ blending into the Canadian NG grid network and H₂ tolerances in end-use appliances
Bibliographic record
Abstract
This report presents the codes, standards, and regulations (CSR), R&D needs and gaps, hydrogen tolerance of key components and systems, demo cases, and Technology Development Matrix (TDM) analysis identified and determined for Power-to-Gas (P2G) technology. P2G technology enables hydrogen produced from electrolysis and renewable natural gas (RNG) produced by methanation to be injected into national gas grids, which permits large scale storage of green energy. If economically feasible, methane injection in the grid could represent considerable volumes since RNG complies with grid specifications. However, the amount of direct hydrogen injected into the gas grid is limited by country-specific standards and regulations. In the European Union the maximum is 0-12 vol.% or 0-2 wt.%. A detailed investigation of CSR on the injection of renewable hydrogen and RNG into natural gas (NG) pipelines has clarified current constraints and safety considerations in terms of gas injection, transport and end-use systems.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".