Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,007 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,091 | 0,092 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».