Stage-Discharge Time Series - Calvert Island - Archived
Notice bibliographique
Résumé
Streamflow calculation; a component of the Kwakshua Watersheds Program In natural streams it is not possible to continuously measure stream discharge, thus an indirect approach was used, where river height (stage) was continuously measured at a gauging station using a pressure transducer, with periodic manual measurements of discharge along the range of potential stages to develop a stage-discharge rating curve. Low flows were manually measured using the velocity-area method, with either a Swoffer Current Velocimeter or a Sontek Acoustic Doppler Velocimeter. Moderate to high flows (generally greater than 1cms) were measured using the salt dilution method, either manually (dry salt) and/or remotely (starting in the fall of 2015), using a fully automated system to release pre-defined volumes of salt solution at pre-defined water stages at an upstream location, with permanently installed electrical conductivity sensors located down-stream, one on either side of the stream to measure the salt wave passing through. Data are available in near real-time using the Hakai Telemetry Network (Floyd and Brunsting, 2015). A calibration factor, required for the salt dilution method, was manually calculated at a minimum twice per barrel refill of salt solution, once at the initial fill and the other with the remaining solution before re-fill. All discharge measurements were 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 were excluded from further analysis. The remaining discharge-stage measurements were plotted as a power-law equation (Q = Ce*(H-h0)^A) in excel, to analyze if there were clear outliers, to determine the approximate value of h0 and to determine if the data could be fitted on one curve, or if they would fit better on a low flow and high flow curve, separated by an 'inflection point'. After this, the rating curve equation was optimized using a non-linear least-squares fitting Python model (LMFit, 2015). A detailed description of these methods have been documented in the MSc thesis of Maartje Korver (2015). Finally, this discharge time-series was created using 5 minute average stage measurements. Extra caution must be taken when using calculated discharges greater than the highest measured discharge (noted in this file as 'Max measured discharge' ), because the extrapolation of a rating curve beyond a set of measurements is usually highly uncertain and can greatly over or under estimate discharge. THESE DATA are provided AS IS and will continuously improve as additional discharge measurements are taken. Users should re-check for periodic updates to the rating curves and subsequent discharge files. If errors are found please contact Bill.Floyd@viu.ca.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,008 |
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 source (Gemma direct ou Codex distillé), 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 ».