Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="htmlview paragraph">All alternate transportation fuels require infrastructure to support widespread use of the new fuel. For example, in Canada in the mid-1980's, NGV markets were being developed in a number of urban settings in Canada supported by fuelling stations offering NGV and by conversion dealers who could install aftermarket conversions and maintain them. Canada's major NGV equipment supplier, CNG Fuel Systems, helped to create the infrastructure by co-investing in fuelling stations and creating a network of authorized dealers. Profitability issues arose for each. For refueling stations, the rate of buildup of fuel usage per station became the key financial performance criterion. For conversion dealers, initial pricing strategies (which reflected traditional margins in auto parts) created too large a markup for conversions, which limited sales prospects. Modified approaches to conversion pricing focused on constant “bay day” revenue for the dealer, with limited success. In each case, the objective was to develop an overall market infrastructure in which each component was sufficiently profitable to motivate ongoing investment. Failure to achieve profitability in the infrastructure components affects the growth in usage of an alternate fuel.</div>
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it