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
Core Ideas Sustainability can only be measured over long time periods. To evaluate sustainability, therefore, we need a way of keeping track—a memory. Soil offers such a memory because the soil stays. An underlying aim of soil science is to extract and describe soils' memories. Terrestrial ecosystems worldwide face mounting stresses and upheavals, mostly from human demands and interferences. Our search for better ways of living on these lands, however, is constrained by a most vexing variable: time . The final outcomes of our management choices—well intended or not—will often fully emerge only after decades, when we are no longer there to see them. A way around this dilemma is to view a longer span of time by studying the land's own memories, notably those in the soil. Most ecosystem elements flicker and fade, much as we do, but the soil stays, … and the soil remembers. In this essay, I propose that the fundamental aim of soil science is to extract and describe the narratives embedded in soils' memories, and I ponder some ways of doing that. This guiding motive, perhaps, may offer hope not only for our science but also for our lands everywhere—on which we and those who follow us will always depend.
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 | 0.002 | 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 | 0.002 | 0.012 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it