Bibliographic record
Abstract
Geochemical data has a major role in ecological and human health risk assessments. The final soil quality guideline is the lowest concentration value for individual elements deemed acceptable for different types of land use. In addition to the inherent mineralogical characteristics of the soils, concentration values resulting from geochemical analyses are strongly affected by the methodologies used for sample preparation and analyses. However, in most guidance documents for risk assessments, little information is provided on the requirements for sample preparation and analysis. The choice of appropriate methodology should include consideration of the following factors: (1) the grain-size fraction analyzed and the efficiency of the grain-size separation. Chemical partitioning studies indicate the greatest concentrations of specific minerals occur within certain grain sizes in glacial sediments. Selection of grain-size fraction is based on the needs of the geochemical survey. The <2 mm fraction is a standard for agricultural and environmental studies and its use is recommended to provide consistency. Finer size fractions including <63 micron and <2 micron have been used for mineral exploration and geological research. Use of the finer size fractions may provide more information on bioaccessibility and inferred information on speciation. (2) the weight of the sample analyzed. Sample size affects analytical precision. There is a minimum weight of sample for individual analytical methods to achieve reliable, representative and reproducible results. (3) the temperature of sample drying and analyses. Volatile elements, such as Hg and As require low temperature storage, preparation, and analytical techniques. Air drying at less than 30o C is recommended. (4) the chemical digestions and other treatments used to decompose the sample prior to analysis. Chemical digestions include total (e.g., 4 acid), near-total (e.g., the relatively strong Aqua Regia and its variants), partial selective extractions for specific mineral or organic phases, and the weak water leach. Digestions are selected based on research goals and geological factors related to mineralogy and they are critical to the application of geochemical analyses results. (5) the instrument used for analysis. There are common methods, each of which are advantageous in some situations and unsuitable in others. They include the widely-used inductively coupled plasma-optical emission spectrometry (ICP-OES) for major elements and inductively coupled plasma - mass spectrometry (ICP-MS) for trace and minor elements; instrumental neutron activation analyses (INAA) that provides total element concentrations; atomic absorption spectrometry (AAS) that has historical and specialized uses, and x-ray fluorescence (XRF) which is used to measure major elements in whole rock. (6) the procedures used to ensure quality control and quality assurance (QA/QC). These include the use of blind duplicates and controlled reference materials inserted for analysis in batches of sample for routine analysis. QA/ QC results are monitored to ensure they fall within pre-determined tolerances to ensure adequate data quality. Both graphical monitoring tools and statistical summaries are available. When data fall out of tolerance it is essential that situations are discussed with the service laboratory to rectify any problems.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.091 | 0.092 |
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".