Identification and Abundance of Barkley Canyon Megafauna: Daily Observations from 2012 to 2015 Using Ocean Network Canada Videos (BC, Canada)
Notice bibliographique
Résumé
This dataset provides comprehensive records of species identified and their daily abundance in three deep-sites (Upper Slope -400m, Wall -900m, and Axis -1000m) of the Barkley Canyon (British Columbia, Canada) from 2012 to 2015. The data was collected through video recordings from the Ocean Network Canada observatory, which are publicly accessible using their SeaTube website. The dataset includes: - a detailed list of species = "species_list_match.csv" - a corresponding table of species names = "Species_details.csv" - daily abundance counts for each sites = "Abundance_SiteName.csv" - a file that outlines the methodology used for identification and counting = "ReadMe.txt" ---------- Method: - During each daily sampling, all 5-minute recordings made between 08:00 and 08:05 were viewed using VLC 2.0.1 © software to count and identify individuals at the lowest possible taxonomic level. All videos (2 minutes fixed + 3 minutes of scanning) were utilized. Videos were deemed unusable if viewing conditions were poor or particle counts were too high. When identification was not feasible, OTUs were defined. On the three sites mentioned, a camera mounted on a tripod recorded continuously at 5-minute intervals each day of the year. During these recordings, the background was illuminated using two spotlights. The camera recorded fixedly for the initial two minutes and could rotate from 0° (stationary, upper slope, pod2) to 360° (full rotation, canyon axis pod1, except between August 2014 and January 2015) and 180° (canyon wall, pod4). The illumination and zoom parameters, adjustable via the ONC site, were not consistent across all sites and sampling periods, affecting the observed surface area, ranging from 0.5 m² (upper slope, pod2 between May 2013 and May 2014) to a maximum of 9 m² (canyon axis, pod1 between mid-2013 and February 2014). The surface area was calculated each time the camera settings were changed (usually during annual maintenance missions) using a scaling grid developed from the camera’s two lasers, fixing a width of 10 cm on the ground as measured in the camera's field of view. Image captures were taken at intervals to cover the entire surface swept by the camera during rotation, estimating the sampled surface by overlaying the scaling grid on each image. - For more details on the methodology and to understand the context in which the data were collected (scientific questions, hypotheses, and studies conducted), you can refer to Pauline Chauvet's thesis (available in open access).
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,004 |
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 ».