Bibliographic record
Abstract
As the number of future LNG projects—from those being constructed to speculative projects in early stages—grows globally, potential LNG buyers, project financiers, investors, partners, host governments, and contractors are struggling to evaluate which projects are more likely to be successful and thus deserving of their attention. Not all projects promoted by a particular company are of equal quality. The author has developed an easy-to-use and easy-to-understand scoring system that is neutral, objective (as much as possible), quantitative, and adaptable based on about 30 criteria, grouped into four categories:Upstream: including criteria such as 1P/3P reserves, NGL%, CO2%, access to reserves, distance to field.Above ground: including host government support, terrorist/violent activity, native rights, taxation stability, political support, government reputation, labour productivity/availability, environmental sensitivity.Technical: technology risk, contractor experience, infrastructure, and engineering stage.Company and market: operator/partner experience, type of off-taker, buyer experience, partner alignment. The scores can be weighted according to the audience priorities. Scoring represents a particular time and its score will change accordingly as a project progresses. Most Australian LNG projects being constructed, designed, and proposed will be evaluated and ranked according to the scoring system above, with up-to-date scores at the time of APPEA 2013. In addition, it is expected that key projects in other countries (in East Africa, North America, East Mediterranean) will also be evaluated to compare their rankings with Australian projects.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".