Notice bibliographique
Résumé
What's worse—finding out the beach is closed due to high levels of pathogens when you arrive at your hotel for a long-awaited vacation, or finding out the beach is closing on your way out of town after swimming all weekend? Minimizing risk and exposure requires being aware of what is in the water. Backpackers assume that all streams and lakes are contaminated and boil, filter, or otherwise treat water from these sources for cooking and drinking. However, recreational water bodies, where people swim and fish, are assumed to be safe for those activities until tests show pathogens are present. Irrigation water is also of concern—the presence of microbes in irrigation water on leafy greens or other produce can cause people to get sick when these goods are consumed. Researchers in the field of microbial water quality address these issues and others. They work on identifying sources of contamination, developing mitigation strategies, predicting high levels of bacteria based on precipitation, tides, and temperatures to name a few. Microbial water quality is a global issue and is the basis of a new special section in the Journal of Environmental Quality (JEQ), “Microbial Water Quality—Monitoring and Modeling.” This special section includes papers from 12 countries and provides a global perspective on microbial water quality research. Yakov Pachepsky, a researcher with the USDA who investigates microbes in irrigation water, worked with a group of guest editors from Canada, Korea, France, Spain, the Netherlands, and the U.S. to put together this special section. Pachepsky, a member of the ASA and SSSA, says there are four facets of microbial water quality—diagnostics, monitoring, modeling, and management—and researchers are working to improve each one. The most common diagnostic tools to detect fecal contamination use the presence of E. coli as an indicator. While E. coli is a common indicator organism, important complementary information on microbial water quality can be obtained using other organisms, genes, or DNA sequences as indicators. Using additional indicators becomes possible with advances methodology and may eventually bring innovative changes in monitoring design and implementation. To improve monitoring, researchers focus on the time it takes to get results back from a sampling event and locations where samples have to be taken. Currently, it can take a day for samples to be processed in a lab. So even with daily sampling, there is a lag between the time pathogens arrive at a site and the time that test results show the water is not safe. This lag time can expose people who are drinking or swimming in water that contains harmful microbes. While new methods are being developed, they may not be implemented quickly due to the potential expense involved in adopting new technology. Modeling tools used to predict when and where microbes may be present in drinking, irrigation, or recreational water are being improved. As data sets increase and modeling tools are improved, researchers are testing new ways to predict outbreaks. Improved models are used to compare and select the mitigation measures, management decisions, and interventions that are geared to minimizing exposures before there is a problem. Management requires identifying sources of contamination. It could be from agricultural land, industry, or aging urban stormwater systems overwhelmed by heavy precipitation. Once the source is identified, management actions can be developed. This can include fencing livestock out of streams, improving filtration in the water system, or improving urban water flow. Pachepsky explains that the search is under way for more cost-efficient new management processes. Pachepsky says that microbial water quality, and minimizing citizen exposure to pathogens through drinking water, recreation, and fresh produce, is a topic that has widespread support. Both citizens and governments agree that there is a need for standards and even regulation. Publishing these papers together also demonstrates that the challenges in studying microbial water quality are universal. This special section will be published in its entirety in an upcoming issue of The Journal of Environmental Quality. A number of papers are currently available in the “Just Published” section of the journal: https://bit.ly/1U5yldx.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 tête enseignante, 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 ».