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Enregistrement W3164681043 · doi:10.7939/r3-pwv3-nf92

Laboratory and Field Measurements of Frazil Ice Characteristics

2019· article· en· W3164681043 sur OpenAlexaboutno aff
Vincent McFarlane

Notice bibliographique

RevueUniversity of Alberta Library · 2019
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueArctic and Antarctic ice dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGeologyField (mathematics)GeomorphologyMathematics

Résumé

récupéré en direct d'OpenAlex

Measurements of frazil ice characteristics in both laboratory and field environments have each been hindered by different challenges to date. In the laboratory, the resolution of the digital imaging systems used to photograph suspended particles has limited the size of the smallest frazil ice crystals that could be observed. In field settings there has not been a practical method by which to directly measure in-situ frazil ice particles due to the difficulty of capturing clear, underwater photographs in harsh winter conditions. As a result, most field studies to date have been carried out using acoustic devices to detect suspended particles. However, these measurements require direct observations for calibration and validation. This study was designed to overcome the challenges faced by previous studies in order to measure complete size distributions of frazil ice particles throughout the supercooling process at various turbulence intensities and in various rivers.A series of laboratory experiments were conducted in which frazil ice particles were produced at three different turbulence intensities. The water temperature was measured and high-resolution, cross-polarised digital images of suspended frazil crystals as small as 22 μm were captured throughout each experiment. An image processing algorithm was written to analyse the frazil ice images and calculate the moving average mean and standard deviation of the particle diameter, and the number of suspended particles throughout the supercooling process. The mean particle diameter was calculated to be 0.94, 0.66, and 0.59 mm with standard deviations of 0.73, 0.51, and 0.45 mm at turbulent kinetic energy (TKE) dissipation rates of 23.9, 85.5, and 336 cm2/s3, respectively. The mean particle size was observed to reach a maximum shortly after the maximum degree of supercooling was reached, then decrease and remain at a constant value during the residual supercooling phase. A lognormal distribution was a good fit to the particle size distribution at all stages of the supercooling process.A digital imaging system, called the FrazilCam, was designed and constructed for use in field environments. The FrazilCam was successfully deployed in the Kananaskis, Peace, and North Saskatchewan Rivers in Alberta. Images captured using the FrazilCam in the first deployment season in 2014-15 were analysed and it was discovered that suspended sediment particles with diameters on the order of 0.1 mm were visible in the images and indistinguishable from ice. This issue was overcome by training support vector machine (SVM) algorithms to identify the differences between sediment and ice particles in each river. The SVM algorithms were able to classify sediment particles with 98% accuracy and remove them from the frazil ice size distributions. Using the SVM algorithms, data from the 2014-15, 2015-16, and 2016-17 freeze-up seasons were analysed. The mean particle diameter was found to range from 0.63 to 1.32 mm during the principal supercooling phase, and from 0.32 to 0.93 mm during the residual supercooling phase. Additionally, the number concentration of suspended frazil crystals varied from 1.48 × 104 to 1.81 × 106 particles/m3. Assuming a constant particle aspect ratio of 37, the volume concentration was estimated to range from 1.0 to 18 × 10−6 m3/m3. Time-series data collected using the FrazilCam indicated that the mean particle diameter and concentration remain approximately constant throughout the residual supercooling phase, and a lognormal distribution was confirmed to describe all of the size distributions calculated under steady flow conditions. A unique supercooling event was recorded during one of the FrazilCam deployments in which the maximum degree of supercooling was −0.145°C. On this occasion ice predominantly grew as shard-like crystals on submerged objects including the bed material rather than suspended disc-shaped frazil crystals.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

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

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.

Tête enseignante Opus0,006
Tête enseignante GPT0,151
Écart entre enseignants0,145 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2019
Routes d'admission1
Résumé présentoui

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