Bibliographic record
Abstract
Andrew Z. Kryzan with Gulf Canada shot me a note regarding the source and accuracy of the quote, “Those who do not learn from history are doomed to repeat it.” He cites a similar quote attributed to Sir Winston Churchill, “In order to look forward intelligently, it is necessary to have looked backward perceptively.” Thanks, Andrew. That is good advice for the people who are “setting” our energy policy in Washington. My gas bill last month was over US$300! Now, I know that natural gas is high priced these days, but I live in Houston where the weather is moderate, where there is a lot of gas and gas lines. I can only guess the cost for heating a house in Wisconsin or Alaska. What's going on? I thought we had a lot of natural gas. An economist, speaking at the AAPG convention many years ago, impressed me greatly by his message (and I misquote): “Don't tell me there is a surplus of a commodity. Tell me that there is a surplus at a given price. Don't just tell me there is a scarcity of natural gas. Tell me at what price the scarcity occurs.” At US$1.25, gas became scarce. At US$8, everyone is out looking for it. To heck with oil, give me gas any day. I assume you remember as far back as the 1950s. When you found gas, you either abandoned the well or shut it in for years, paying shut-in costs to the royalty owners. I can't imagine anyone shutting in a gas well today.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".