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Enregistrement W6907714392 · doi:10.21966/fh63-w427

Observed stream flow from seven small coastal watersheds in British Columbia, Canada, Sept 2013 - Sept 2019

2013· dataset· en· W6907714392 sur OpenAlex

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Notice bibliographique

RevueHakai Institute · 2013
Typedataset
Langueen
Domaine
Thématique
Établissements canadiensVancouver Island UniversityMcGill University
Organismes subventionnairesnon disponible
Mots-clésRating curveHydrology (agriculture)STREAMSFlow measurementPressure sensorTransducerWatershedDilutionFlow (mathematics)

Résumé

récupéré en direct d'OpenAlex

General field methods In natural streams it is not possible to continuously measure stream discharge, thus an indirect approach was used: river height (stage) was continuously measured at a gauging station using a pressure transducer and periodic discharge measurements were taken along the range of potential stages to develop a stage-discharge rating curve. Detailed description of the measurement methods outlined below can be found in the accompanying document "Methods and metadata for discharge time-series version 5.0." Pressure transducers were installed in the fall of 2013 at watershed 708 and in the fall of 2014 at the other watersheds. Low flows were manually measured using the velocity-area method, with either a Swoffer Current Velocimeter or a Sontek Acoustic Doppler Velocimeter. Stream flows, generally greater than 0.5 m3/s, were measured using the salt dilution method, either manually (dry salt) or remotely (starting in the fall of 2015) using a fully automated system. The automated salt dilution (auto-salt) system releases pre-defined volumes of salt solution at pre-defined water stages, with two electrical conductivity sensors permanently located down-stream, to measure the salt wave passing through. Data are available in near real-time using the Hakai Telemetry Network (www.hakai.org/technology/#science-1). General data QC and analysis Stage-discharge rating curves are not static but shift over time due to changes in the morphology of river channels, often associated with flood events. Therefore, rating curves are updated regularly, notably after high-flow events. All discharge measurements are assigned a relative uncertainty, based on fluctuations in the flow velocity profile (for area-velocity method), or based on the uncertainty in the volume of salt solution, the EC sensor resolution and the EC sensor calibration factor (for salt dilution method). Measurements with uncertainties higher than 20%, with noise or malfunctioning conductivity sensors, or with high uncertainties in stage monitoring are excluded from further analysis. The remaining stage-discharge measurements are plotted using a LOESS regression that accounts for scatter in the stage-discharge data and multi-section rating curves. Uncertainty of derived discharge data is quantified by plotting confidence intervals (CI) around the rating curve. Following the methodology proposed by Coxon et al. (2015), these CI's are derived from 500 curve fitting results of LOESS regressions on a randomized set of stage-discharge measurements and their maximum and minimum value of error. Using LOESS regression is considered an improvement from using fixed power-law shaped functions (previously used method), as LOESS has no defined shape and can therefore fit data more precisely. Especially the determination of confidence intervals using LOESS provides more realistic results as the previous CI algorithm is intended for linear functions and therefore needs to be log transformed. This results in unrealistic small CI's in the low flow end and unrealistic high CI's in the high flow end of the rating curve. This discharge time-series was created using 5-minute average stage measurements that are Quality Controlled (QC), flagged and corrected where needed. Generally, data gaps that were filled as well as noisy, faulty data that were corrected were assigned an ‘EV’ – Estimated Value flag. Suspicious data points that could not be corrected and estimated were assigned an ‘SVC’ – Suspicious Value Caution flag. All other data points were flagged ‘AV’ – Accepted Value. QC flags assigned to stage data were automatically copied to the corresponding 5-minute discharge calculations. Only flows greater than the highest measured discharge were assigned an additional 'SVC' flag, because the extrapolation of a rating curve beyond a set of measurements is usually highly uncertain and can greatly over or under estimate discharge. Hourly, daily, monthly and yearly discharge rates, as well as hourly, daily, monthly and yearly discharge volumes are calculated from 5-minute discharge data as described in Table 3. Open access calculation scripts The R scripts used to calculate the rating curves as well as the hourly, daily, monthly and yearly discharge rates are available on Github: - https://github.com/HakaiInstitute/RatingCurve - https://github.com/HakaiInstitute/Discharge-editing Versioning Discharge v5 includes time-series up to October 1st, 2019. Methods and rating curves are identical to those used in version 4.1. References Coxon, G., J. Freer, I. K. Westerberg, T. Wagener, R. Woods, and P. J. Smith.: A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations, Water Resour. Res., 51, 5531–5546, doi:10.1002/2014WR016532, 2015.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Communication savante, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,138
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,002
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0020,001
Science ouverte0,0030,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0090,014

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.

Tête enseignante Opus0,024
Tête enseignante GPT0,206
Écart entre enseignants0,183 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

En bref

Citations1
Publié2013
Routes d'admission2
Résumé présentoui

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