Gas Storage Technology Consortium
Bibliographic record
Abstract
The EMS Energy Institute at The Pennsylvania State University (Penn State) has managed the Gas Storage Technology Consortium (GSTC) since its inception in 2003. The GSTC infrastructure provided a means to accomplish industry-driven research and development designed to enhance the operational flexibility and deliverability of the nation's gas storage system, and provide a cost-effective, safe, and reliable supply of natural gas to meet domestic demand. The GSTC received base funding from the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) Oil & Natural Gas Supply Program. The GSTC base funds were highly leveraged with industry funding for individual projects. Since its inception, the GSTC has engaged 67 members. The GSTC membership base was diverse, coming from 19 states, the District of Columbia, and Canada. The membership was comprised of natural gas storage field operators, service companies, industry consultants, industry trade organizations, and academia. The GSTC organized and hosted a total of 18 meetings since 2003. Of these, 8 meetings were held to review, discuss, and select proposals submitted for funding consideration. The GSTC reviewed a total of 75 proposals and committed co-funding to support 31 industry-driven projects. The GSTC committed co-funding to 41.3% of the proposals that it received and reviewed. The 31 projects had a total project value of $6,203,071 of which the GSTC committed $3,205,978 in co-funding. The committed GSTC project funding represented an average program cost share of 51.7%. Project applicants provided an average program cost share of 48.3%. In addition to the GSTC co-funding, the consortium provided the domestic natural gas storage industry with a technology transfer and outreach infrastructure. The technology transfer and outreach were conducted by having project mentoring teams and a GSTC website, and by working closely with the Pipeline Research Council International (PRCI) to jointly host technology transfer meetings and occasional field excursions. A total of 15 technology transfer/strategic planning workshops were held.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.225 | 0.116 |
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".