Synthesis to Special Issue on New Studies in EROI (Energy Return on Investment)
Bibliographic record
Abstract
This paper is a synthesis of a series of twenty papers on the topic of EROI, or energy return on investment. EROI is simply the energy gained from an energy-obtaining effort divided by the energy used to get that energy. For example, one barrel of oil invested into getting oil out of the ground might return fifty, thirty, ten or one barrel, depending when and where the process is taking place. It is meant to be read in conjunction with the first paper in this special issue and also a number of the papers themselves. As such I try to summarize what general trends we might conclude from these varied and often highly technical papers. About half of the papers are reports on empirical analyses of various energy sources such as Norwegian or Gulf of Mexico oil, Pennsylvania gas and so on. About a quarter of the papers are methodological: how do we go about undertaking these analyses, what problems are there, what are the proper boundaries and so on. The final quarter are in a sense philosophical: since it appears that we will be living indefinitely in a world of decreasing EROIs, what are the economic, social and psychological implications? The rest of this paper summarizes the results of these studies.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.081 | 0.015 |
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".