Bibliographic record
Abstract
There are two ways to exploit natural systems. Rip-off or restore. While self-serving euphemisms (e.g., "green," "sustainability," "reforestation," and "the" ecology) may be as repulsive to purists as they are appealing to reformers and spin-hypers alike, they do serve the purpose of laying a stone or two toward bridging the gap between the rampant masses and the enlightened. So what's the next stepping stone between scientific understanding, applications that work, and broad enough acceptance of the implications of the applications and the science behind them? In most of my work (highway cuts and fills, pipelines, landfills, and the like), "equilibrium" is not an issue. The patient is, theoretically, dead. Therefore, equilibrium, or at least the initiation and acceleration of a trend that is in that direction, becomes the issue. The issue is: "Is restoration necessary?" What measures beyond the incredible resilience of natural processes, given enough time, are feasible, and will restoration of the ecosystem equilibrium be the result? How closely will, and how closely can, the "restored" ecosystem resemble the "original?" When is equilibrium achieved? The scientifically stringent performance criteria proposed independently by both St. John and Ewel in the 1980s require that a succesful ecosystem restoration project must: (1) be capable of perpetuating itself without outside subsidy (no irrigation or fertilizer); (2) be resistant to long-term weed invasion; (3) closely match the original ecosystem's productivity; (4) recycle nutrients; and (5) exhibit the entire range of critical biological components. These criteria should stand until improved. How are these criteria to be measured and judged? Any takers?
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.058 | 0.024 |
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".