Human-Soil Relations are Changing Rapidly: Proposals from SSSA's Cross-Divisional Soil Change Working Group
Bibliographic record
Abstract
A number of scientists have named our age the Anthropocene because humanity is globally affecting Earth systems, including the soil. Global soil change raises important questions about the future of soil, the environment, and human society. Although many soil scientists strive to understand human forcings as integral to soil genesis, there remains an explicit need for a science of anthropedology to detail how humanity is a fully fledged soil-forming factor and to understand how soil change affects human well being. The development and maturation of anthropedology is critical to achieving land-use sustainability and needs to be nurtured by all soil disciplines, with inputs from allied sciences and the humanities,. The Soil Science Society of America (SSSA) has recently approved a cross-divisional Working Group on Soil Change, which aims to advance the basic and applied science of anthropedology, to facilitate networks of scientists, long-term soil field studies, and regional databases and modeling, and to engage in new modes of communications about human–soil relations. We challenge all interested parties, especially young scientists and students, to contribute to these activities and help grow soil science in the Anthropocene.
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 | 0.052 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.055 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".