Bibliographic record
Abstract
Abstract A novel method is presented for indexing and normalizing natural gas endowment. The approach is demonstrated with data from Latin America and Caribbean (LAC) countries. Endowment, as defined by the USGS, refers to the sum of known volumes of hydrocarbons (cumulative production plus remaining reserves) and undiscovered volumes. The method uses a variable shape distribution model (VSD) to fit the conventional natural gas endowment published by the USGS (2000) for 29 petroleum provinces in LAC countries. The fits are good with coefficients of determination (R2) greater than 98% in all cases. The data are indexed and normalized to generate tables and crossplots of number of petroleum provinces versus normalized endowments for LAC countries. The LAC curves are compared with normalized endowments from other petroleum provinces in regions around the world, including North America, Europe, Asia Pacific, the Middle East, NorthAfrica, and the former Soviet Union. The results give the method predictive power for estimating conventional natural gas endowment in LAC petroleum provinces that at present have very small to negligible exploration activity. Of particular importance, from a practical point of view, is the fact that all the real data and VSD curves for the petroleum provinces of the various regions considered in this study display generally a concave pattern throughout, except for the curve of Latin America and the Caribbean, which displays a distinct convex pattern at the largest values of gas endowment. The complete LAC curve displays the shape of an inverted "S". The comparison suggests that there is potentially a gigantic volume of gas in the region that has not been considered in previous studies.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".