AAS Birmingham 2022 live streaming online Cheer & Dance free
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Notice bibliographique
Résumé
St. Helena’s Legacy Dance Collective is one of 10 groups signed up to participate in the seventh annual Day of Dance and Cheer, hosted by the Napa High School Spiritleaders on Sunday, Dec. 11. The largest dance event in the county, with more than 500 participating last year, it will be held in Messner Gym starting at noon. Doors open at 11:30 a.m.\n\n\n\t\n\t\n\tLIVE: CHEER & DANCE STREAMING ONLINE \n\t\n\t\n\n\nVersion 143 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.rtyrt\n\nHollie Johnson, Napa High School dance director, created the event to showcase all of the talent in the valley and bring unity for those that all share the same passion for dance and cheer. All schools and dance studios are invited to come for free to showcase their favorite routines. Coaches also come for free and are treated to a free lunch.\n\n“We love bringing teams together,” Johnson said. “It’s my dancers’ favorite time of year. They always talk about the supportive environment and the new friends they make.”\n\n2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.tryruj\n\n2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "eetrtuj\n\n2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.ytit\n\n2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.rtyrtuj\n\nThis dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:yu\n\nΤhe CORD-19 dataset released by the team of Semantic Scholar1 and\nΤhe curated data provided by the LitCovid hub2.\nThese data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 501,088 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:\n\nInfluence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyu\n\nInfluence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.\nPopularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.\nPopularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyt\n\nSocial Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.\nWe provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).tyu\n\nThe work is based on the following publications:tuy\n\nNCA & NDA Northeast Regional Championship 2022 live streaming online Cheer free\nThe American Grand Grand Nationals 2022 live streaming online Cheer free\nSpirit Cheer Dance Grand Nationals & Cheer 2022 live streaming online Cheer free\nEncore Baltimore Showdown 2022 live streaming online Cheer free\nCHEERSPORT Oaks Classic 2022 live streaming online Cheer free\nAloha Gatlinburg Showdown 2022 live streaming online Cheer free\nASC Battle Under the Big Top Grand National 2022 live streaming online Cheer free\nSpirit Sports Worcester- National 2022 live streaming online Cheer free\nUDA DC Dance Challenge 2022 live streaming online Cheer free\nNation's Choice Wisconsin Dells Grand National 2022 live streaming online Cheer free\nCHEERSPORT Greensboro State Classic 2022 live streaming online Cheer free\nACP Columbus Showdown 2022 live streaming online Cheer free\nUCA Salt Lake City Regional 2022 live streaming online Cheer free\nNCA Holiday Classic 2022 live streaming online Cheer free\nCHEERSPORT Hot Springs Classic 2022 live streaming online Cheer free\nAll Star Challenge Grand Nationals 2022 live streaming online Cheer free\nNation’s Choice Grand Nationals 2022 live streaming online Cheer free\nThe American Grand Nationals 2022 live streaming online Cheer free\nGlobal Events Manheim 2022 live streaming online Cheer free\nAAS Birmingham 2022 live streaming online Cheer free\nFull Out Combat Cheer Homefront Civil Showdown WA 2022 live streaming online Cheer free\nCelebrity Championships Branson 2022 live streaming online Cheer free\nKingdom Events Manheim 2022 live streaming online Cheer free\nMaximum Cheer and Dance PA Madness 2022 live streaming online Cheer free\nWorld Class Cheer WCC Virtual Championship 2022 live streaming online Cheer free\nUCE Dayton Experience 2022 live streaming online Cheer free\nCheer Derby Nashville Nationals 2022 live streaming online Cheer free\nSpirit Brands The Festival Wildwood 2022 live streaming online Cheer free\nUS Cheer Productions Holiday Extravaganza Championships 2022 live streaming online Cheer free\nDeep South Spirit New Jersey Classic 2022 live streaming online Cheer free\nGold Rush Fort Worth 2022 live streaming online Cheer free\nUnited Cheer Events Galveston Championship 2022 live streaming online Cheer free\nSpirit Royale Marquee Los Angeles 2022 live streaming online Cheer free\nMCDA Cowboy Christmas Classic West Monroe LA 2022 live streaming online Cheer free\nValley of the Sun Shake Your Palm Palms 2022 live streaming online Cheer free\nCheer Evolution Montreal Mayhem 2022 live streaming online Cheer free\nBaby I’m a Star Christmas Spectacular 2022 live streaming online Cheer free\nJAMZ Showdown @ The Bay 2022 live streaming online Cheer free\n9 Panel Cheer All Star Jam Concord 2022 live streaming online Cheer free\nBravo Spirit Christmas Classic 2022 live streaming online Cheer free\n\nCOVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-11-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-11-25. doi:10.5281/zenodo.3715506\nChen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-11-25)\nR. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab.\nI. Kanellos, T. Vergoulis, D.
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.
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 | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,006 | 0,009 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,064 | 0,005 |
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 découle