Time in a bottle: Use of soil archives for understanding long‐term soil change
Bibliographic record
Abstract
Abstract Soil archives preserve a snapshot of soils from a specific time and location, allowing researchers to re‐evaluate soils of the past in the context of the present for an improved understanding of long‐term soil change. To date, the extent of soil archive use in the peer‐reviewed literature is poorly inventoried. Here, we document the characteristics and distribution of global soil archive use, as found in 245 publications, following an exhaustive search of English language journals. Soil archive use has increased substantially since 1980, reaching 59 publications between 2016 and 2020. The age of soil archives across the compilation ranged from 5 to 160 yr, with mean and median archive ages of 48 and 37 yr, respectively. Publications using soil archives originated mostly from countries in the northern hemisphere, with the top five reporting countries including the United States (61), United Kingdom (52), New Zealand (21), Canada (18), and China (14). Land uses associated with soil archive publications were dominated by agroecosystems, specifically land planted to annual crops. Forty‐seven percent of investigations focused on changes in soil C, N, or organic matter, whereas investigations of other subjects did not exceed 20% each. The compilation is publicly available online. As demands on soils increase, archives will serve as an invaluable tool for understanding long‐term soil change in the Anthropocene era. Multiregional coordination and increased investment in curation and retention of soil archives are recommended to preserve these irreplaceable resources.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".