Bibliographic record
Abstract
Farmlands are expansive, diverse, and intensively managed ecosystems. These lands, so critical to human welfare, are threatened by growing stresses as demand for food escalates, fresh water wanes, cheap fuels deplete, and other uses jostle for space. With these coming pressures, how can we foster permanence on the lands that sustain us? In this essay I contemplate the hypothesis that the greenhouse gases, because they emanate from the interwoven flows of C, N, and energy in ecosystems, can help steer us toward permanence (sustainability). Alongside other indicators these emissions may detect the ecosystem's pulse, alerting us to inefficiencies and guiding us to better practices. To be effective signals, however, the greenhouse gases will need to be considered in their local settings, monitored longer and in more "listening places," and measured across boundaries of disciplines and biomes. This approach may help reduce greenhouse gas emissions from our farmlands. But we may find that, in the long run, the main beneficiaries of our inquiry have been, not just the atmosphere, but our fragile lands, perhaps in ways we cannot yet foresee.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".