Spatial extent of surface canopy kelp derived from fixed-wing surveys (2020), Central Coast, British Columbia, Canada
Notice bibliographique
Résumé
This data package consists of digitized surface canopy extent for two species of canopy forming kelp – giant kelp (Macrocystis pyrifera) and bull kelp (Nereocystis luetkeana) – for two regions on the Central Coast of British Columbia, Canada. Note - this data package is a subset of a greater kelp canopy data package for multiple years. See links in related resources for "Spatial extent of surface canopy kelp derived from fixed-wing surveys (2020-2022), Central Coast, British Columbia, Canada" Spatial datasets of kelp canopy were derived from high resolution aerial imagery (10 cm) collected from fixed-wing aerial surveys. The survey regions included (1) northwest Calvert Island (flown August 23rd), (2) the Goose Group and Gosling Rocks region (flown August 23rd, 2020) and (3) a region from the Simonds group to Triquet Island (flown August 23rd, 2020). Survey windows for imagery collection were considered based on obtaining imagery during maximum kelp extent and during a low tide cycle therefore survey windows were planned between July through September during tides of less than +2.0 m (mean low low water). Imagery Capture: Four-band imagery (red-green-blue-near infrared) was collected using two high resolution cameras aboard the Aerial Coastal Observatory (ACO, - a fixed-wing aircraft) at tide levels less than 2 m (chart datum) during boreal summer. Imagery collection and orthomosaic processing is described in associated reports linked in this record. Kelp delineation: Surface kelp canopy is represented by polygons which were digitized using the Hakai Institute Kelp-O-Matic AI tool. Datasets were reviewed by a expert analyst for quality analysis/quality control purposes. The data package includes: - ACO metadata report (.pdf) which describe the system used to acquire and process imagery. - Polygon shapefiles (.shp) of the extent of kelp canopy at each monitoring site by year served in a . Coordinate system used: NAD1983 UTM Zone 9N These data were collected in partnership between Marine Plan Partnership for the North Pacific Coast (MaPP) and the Hakai Institute. This data package is a component of the MaPP Regional Kelp Monitoring Program (RKMP) whose goals (among many) are to monitor the extent and condition of kelp forests across all four sub-regions in recognition of kelp’s ecological, cultural and economic importance. This data package is also a part of the Hakai Institute's Marine Habitat Mapping Program whose broader goal is to document and understand long-term trends of kelp forests dynamics and drivers at local, regional and coast-wide scales. The use of this dataset requires permission from both MaPP and the Hakai Institute. Please communicate and/or collaborate with Hakai and MaPP if you are considering using this dataset for manuscripts or other forms of analysis and reporting. Contact Luba Reshitnyk (luba@hakai.org) or data@hakai.org for more information about data access and opportunities to collaborate with the Hakai Institute. Contact Sarah Schroeder (sschroeder@mappocean.org) and Genevieve Reynolds (greynoldsccira@gmail.com) for more information about data access to and opportunities to collaborate with MaPP.
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 distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 tête enseignante, 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 ».