Advances in Hydrographic Data Processing: Time for a Paradigm Shift
Notice bibliographique
Résumé
With currently available software, hydrographic data processing can be challenging for even experienced users. Though software generally keeps up with advances in hardware and processing methods, many of the frustrations are due to the fact that the human operator must connect all the pieces together to come up with the final processing solution. The problem in many cases is the human operator. Humans make typing errors when transcribing vessel configurations from one software application to another or from one coordinate frame convention to another. Humans make errors when importing ancillary data and then failing to associate it with the correct data files. Humans make errors when changing processing configurations and not then triggering the appropriate reprocessing or perhaps not triggering it for the correct files. Any human error along the way leads to poor results in the final product and many wasted hours, days or even weeks troubleshooting the source of the error. A paradigm shift is a fundamental change in approach or underlying assumptions. With the release of QPS Qimera, we are striving for a paradigm shift in that we are automating the mundane and error prone tasks for which computers are well suited but humans are not. Examples of where computers beat human operators include data transcription, unit conversion, coordinate frame transposition, processing state management and job scheduling. Qimera isolates the stages for which a human brings value to the process. Examples where humans win out (for the moment) include data integration troubleshooting, processing configuration management and data validation. With these concepts in mind, Qimera was built from the ground up with a Guided Workflow design philosophy that lets non-expert users arrive at typical bathymetric deliverables with little training or expert knowledge. Qimera also provides a Dynamic Workflow in that it is easy to make processing configuration adjustments or to perform data validation and to immediately assess the impacts of changes. The shortened feedback cycle between cause and effect promotes causal reasoning, a key ingredient for natural human learning processes. In effect, it allows users to train themselves. In this talk, we examine common sources of human error and demonstrate potential improvements to workflows with working examples of Qimera in action. Presenter Bio Jonathan Beaudoin has a Ph.D (2010) in Geodesy and Geomatics Engineering from the University of New Brunswick and Bachelor's degrees in Geodesy and Geomatics Engineering (2002) and Computer Science (2002), also from UNB. After finishing his Ph.D, he came to CCOM and did research in the field of echosounding uncertainty associated with oceanographic variability, seabed backscatter processing and improving best practices in multibeam echosounder fleet management as the Principal Investigator of the NSF-funded Multibeam Advisory Committee. After nearly four years at CCOM, Jonathan returned to Fredericton, Canada in 2013 to work for QPS where he is Chief Scientist and Product Manager for FMGT, FM Midwater and Qimera.
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 | 0,071 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,016 |
| Communication savante | 0,016 | 0,037 |
| Science ouverte | 0,005 | 0,013 |
| Intégrité de la recherche | 0,009 | 0,027 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,011 |
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 ».