Bibliographic record
Abstract
Abstract The words we choose to describe our research ultimately directs its course. A dominant term in soil science now, is ‘sequestration’, referring to the removal of carbon (C) from air and its irreversible seclusion in soil, ideally as stable soil organic carbon (SOC). An emerging view, however, now sees SOC as an inherently dynamic assemblage of forms, all potentially vulnerable to decay, with no discrete, measurable fraction holding C in ‘sequestered’ form. Rather than speaking of C ‘sequestration’, then, we might refer instead to SOC ‘stewardship’. This word, now, enfolds the entire spectrum of SOC, not merely some elusive ‘persistent’ or ‘stable’ fraction, perhaps redirecting inquiry; for example, does C need to be ‘sequestered’ in stable form for SOC to serve as effective repository of excess atmospheric CO 2 ? ‘Stewardship’ explicitly accepts the relentless turnover of SOC, emphasizing the need to manage not only fixed stocks of C, but also the cyclical flows of C through ecosystems that drive their functions. Among other benefits, ‘stewardship’ might motivate us to consider all functions of SOC (not only climate mitigation), consider the entire C cycle (not only enhancing soil C), and preserve existing troves of SOC (not only augmenting them in selected places.) Perhaps most fundamentally, by its etymology, ‘stewardship’ poses a compelling, timeless question: for whom do we steward SOC? Asking why look after SOC, not only reflects our own underlying quest for resilience, but also expands our potential audience and entices the more creative minds that must succeed us. Although ‘stewardship’ may elicit new and fruitful inquiry, we may need to look for words even more evocative, more alluring, more true to our mandate of living well within the circling C that must always sustain us.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".