The North American soil geochemical landscapes project -- 2010 US geological survey update
Bibliographic record
Abstract
A detailed knowledge of the concentration of chemicals in soil is required for calculation of human exposure to those chemicals via a soil pathway. At present, agencies involved with human and environmental health have no common understanding of soil geochemical background variation for North America and the processes that control this variation. The North American Soil Geochemical Landscapes Project, a tri-national initiative among the United States, Canada, and Mexico, was established to (1) develop a continental-scale design and protocols for generating soil geochemical data and (2) provide baseline soil geochemical data that are useful for a wide range of applications and disciplines, including public health. The Project is based on low-density sample collection over a spatially balanced array of 13,496 sites for the continent (1 sample site per 1,600 sq. km.). The core samples collected at each site include material from a depth of 0-5 cm and soils from the A and C horizons. In the US each sample is analyzed for more than 40 elements and mineralogy. Through partnerships with other federal agencies new data on the distribution of soil bacteria and microbial biomass are being generated. Preliminary results indicate that concentrations of potentially toxic elements in soils commonly vary by 1-2 orders of magnitude. The observed variability is the result of several parameters such as soil parent material, climate, and human activities. Understanding this variation is critical in terms of understanding human exposure and for understanding soil pollution on a national scale. The USGS is committed to the completion of sample collection in the conterminous United States by the end of 2010 field season.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".