Bibliographic record
Abstract
The global carbon (C) cycle is changing, as evident from abrupt increases in atmospheric CO2. These changes have sparked interest in agricultural soils as potential repositories for excess atmospheric C. Our perspective on soil C, therefore, has shifted: once, we focused mainly on how soil C affected productivity within agroecosystems; now we see also how C dynamics in agricultural soils exert influences far beyond the farm. We have long used soil C as an indicator of soil quality; now we may want to use soil C also as a broader indicator of ecosystem response. To prompt further discussion, I offer some tentative thoughts about how we might use soil C as an indicator on a changing earth. They include: using soil C to measure changes across time, not only across space; devising more sensitive measures of soil C change; quantifying soil C across four dimensions; measuring the nature of C, as well as its amount; using soil C alongside other indicators; finding better ways of admitting our uncertainty; establishing long-term sites for our successors to measure soil C change; and following flows of C past the farm fences. Recent worries about global warming have focused our attention on “sequestering” soil C to remove atmospheric CO2. That aim may be worthy, but perhaps too narrow; a broader goal might be to ensure the productivity, permanence, and health of our agroecosystems and adjacent environments – and use C storage as a measure of progress toward that goal. Key words: Soil organic matter, global carbon cycle, carbon sequestration, global change
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".