Paradigm Shift in Protective Barrier Covering Implements for the Endemic Phase of Corona Virus and Routine Airborne Pollutants: The Game Changer Approach - Phase Three Category
Notice bibliographique
Résumé
This article presented a possible protective solution to the diverse health problems associated with human beings inhaling routine anthropogenic airborne particulates and micro-organisms by introducing some regular user friendly barrier covering implements i.e. narrows it down to zero pollutant inhaled for each person’s health care protection via the introduction of cosmetic-style barrier coverings, for the individual sense organs (i.e. nose-mouth-eye). Comprehensively, the atmosphere air is a mixture of several gases, consisting of three main components (78% nitrogen, 21% oxygen, and 1% argon), water vapor, trace gases such as the noble gases (neon, helium, krypton, and xenon); greenhouse gases (carbon dioxide, methane, nitrous oxide, and ozone); and the other gases such as hydrogen, iodine, carbon monoxide, ammonia, nitrogen dioxide, and sulfur dioxide, etc. Furthermore, particulate matter (PM) a mixture of solid particles and liquid droplets, such as dust, dirt, soot (a.k.a. black carbon), smoke, and smog-causing pollutants such as oxides of nitrogen (NOx), oxides of sulfur (SOx), are regularly being released into the atmosphere by human activities (anthropogenic sources). Along with volatile organic compounds (VOCs) i.e. chemical gases released from solid and liquid chemical products such as detergents, pesticides, printer supplies, adhesives, furniture, electronics, paints (and many other products), gasoline vapors, power plants and automobile exhaust, re-occurring wildfires and bush burning in different parts of the world (e.g. Canada, Brazil, California, etc.). Specifically, the June 2023 Canadian wildfire, whose smoke drifted into the northeast United States, and then temporarily made New York City “the most polluted city on the planet”, plus, the occasional air borne viruses and bacteria diseases (particles and respiratory droplets), during pandemics e.g. influenza, corona virus disease 19 (COVID-19), the common respiratory syncytial virus (RSV-a seasonal virus, characterized by variable epidemiology, depending on geographic area and climate) that share many similar symptoms as corona virus, etc. Most notably, this July 22, 2023 Erika Edwards report on “tripledemic” quoted Dr Mandy Cohen (director of the Centers for Disease Control and Prevention), as saying that the American people are expecting to have three bugs out there, “three viruses: COVID, of course, flu and RSV”. This means that many Americans will be urged to get three different vaccinations this fall: COVID, RSV and the annual flu shot. “But that will be a challenge for the health care system, (said Dr. William Schaffner, an infectious diseases expert and professor of preventive medicine at Vanderbilt University Medical Center), at a time when there’s already vaccine fatigue”. The pollutants and greenhouse gases (GHGs- CO2, CH4, N2O, O3, etc.) do not only contributing to climate change (e.g. global warming the emphasis in my first and second articles) but are also the major air, water, and soil pollution that already afflictsmany cities/countries globally today. Air pollutants with the strongest evidence for public health concern include particulate matter (PM), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2).
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 | 0,005 | 0,001 |
| 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) | 0,000 | 0,000 |
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 ».