Quality-assurance data for routine water analyses by the U.S. Geological Survey Laboratory in Troy, New York—July 1995 through June 1997
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Notice bibliographique
Résumé
For additional information, contact: Director, New York Water Science CenterU.S. Geological Survey 425 Jordan Rd Troy, NY 12180 (518) 285-5695 http://ny.water.usgs.gov/ The laboratory for analysis of low-ionic-strength water at the U.S. Geological Survey (USGS) office in Troy, N.Y. analyzes samples collected by USGS projects in the Northeast. The laboratory’s quality-assurance program is based on internal and interlaboratory quality-assurance samples and quality-control procedures developed to ensure proper sample collection, processing, and analysis. For the time period addressed in this report, the quality-assurance/quality-control data were stored in the laboratory’s SAS data-management system, which provides efficient review, compilation, and plotting of quality-assurance/quality-control data. This report presents and discusses samples analyzed from July 1995 through June 1997. Quality-control results for 19 analytical procedures were evaluated for bias and precision. Control charts show that data from ten of the analytical procedures were biased throughout the analysis period for either high-concentration or low-concentration samples but were within control limits; these procedures were: acid-neutralizing capacity, total monomeric aluminum, ammonium, calcium, chloride, dissolved organic carbon, magnesium, nitrate (ion chromatography), nitrate (colorimetric method), and sulfate. Four of the analytical procedures were occasionally biased but were within control limits; they were: fluoride, pH, silicon, and sodium. Results from the filter-blank and analytical-blank analyses indicate that all analytical procedures in which blanks were run were within control limits, although values for a few blanks were outside the control limits. Sampling and analysis precision are evaluated herein in terms of the coefficient of variation obtained for triplicate samples in 14 of the 19 procedures. Data-quality objectives (DQO’s) were met by at least 92 percent of the samples analyzed in all procedures except acid neutralizing capacity (80 percent of samples met objectives), total monomeric aluminum (87 percent of samples met objectives), organic monomeric aluminum (89 percent of samples met objectives), and chloride (89 percent of samples met objectives). The data are insufficient to evaluate the DQO’s for total aluminum. Results of the USGS interlaboratory Standard Reference Sample Program indicated acceptable data quality for most constituents over the time period. The results of the P-sample (low-ionic strength constituent) analysis indicated high data quality with good ratings in all studies. The T-sample (trace constituent) had unacceptable ratings in two studies, but received satisfactory ratings in the others. The N-sample (nutrient constituent) studies had an unacceptable rating in one and an excellent rating in the other. Environment Canada’s NWRI program results indicated that at least 90 percent of the samples met data-quality objectives in 9 of the 12 analyses; exceptions were calcium, chloride, and silicon. Data-quality objectives were not met for calcium samples in two NWRI studies, but all of the samples analyzed were within control limits for the remaining studies. Data-quality objectives were not met for 32 percent of samples analyzed for chloride and 27 percent of samples analyzed for silicon. Results from blind reference-sample analyses indicated that data-quality objectives were met by at least 90 percent of the calcium, pH, potassium, and sodium samples. Data-quality objectives were met by 77 percent of the chloride samples, 83 percent of the magnesium samples, and 80 percent of the sulfate samples. There is insufficient data to evaluate the specific conductance samples.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
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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 découle