Over 10 million seawater temperature records for the United Kingdom Continental Shelf between 1880 and 2014 from 17 Cefas (United Kingdom government) marine data systems
Notice bibliographique
Résumé
Abstract. The datasets described here bring together quality-controlled seawater temperature measurements from over 130 years of departmental government-funded marine science investigations in the UK (United Kingdom). Since before the foundation of a Marine Biological Association fisheries laboratory in 1902 and through subsequent evolutions as the Directorate of Fisheries Research and the current Centre for Environment Fisheries & Aquaculture Science, UK government marine scientists and observers have been collecting seawater temperature data as part of oceanographic, chemical, biological, radiological, and other policy-driven research and observation programmes in UK waters. These datasets start with a few tens of records per year, rise to hundreds from the early 1900s, thousands by 1959, and hundreds of thousands by the 1980s, peaking with > 1 million for some years from 2000 onwards. The data source systems vary from time series at coastal monitoring stations or offshore platforms (buoys), through repeated research cruises or opportunistic sampling from ferry routes, to temperature extracts from CTD (conductivity, temperature, depth) profiles, oceanographic, fishery and plankton tows, and data collected from recreational scuba divers or electronic devices attached to marine animals. The datasets described have not been included in previous seawater temperature collation exercises (e.g. International Comprehensive Ocean–Atmosphere Data Set, Met Office Hadley Centre sea surface temperature data set, the centennial in situ observation-based estimates of sea surface temperatures), although some summary data reside in the British Oceanographic Data Centre (BODC) archive, the Marine Environment Monitoring and Assessment National (MERMAN) database and the International Council for the Exploration of the Sea (ICES) data centre. We envisage the data primarily providing a biologically and ecosystem-relevant context for regional assessments of changing hydrological conditions around the British Isles, although cross-matching with satellite-derived data for surface temperatures at specific times and in specific areas is another area in which the data could be of value (see e.g. Smit et al., 2013). Maps are provided indicating geographical coverage, which is generally within and around the UK Continental Shelf area, but occasionally extends north from Labrador and Greenland to east of Svalbard and southward to the Bay of Biscay. Example potential uses of the data are described using plots of data in four selected groups of four ICES rectangles covering areas of particular fisheries interest. The full dataset enables extensive data synthesis, for example in the southern North Sea where issues of spatial and numerical bias from a data source are explored. The full dataset also facilitates the construction of long-term temperature time series and an examination of changes in the phenology (seasonal timing) of ecosystem processes. This is done for a wide geographic area with an exploration of the limitations of data coverage over long periods. Throughout, we highlight and explore potential issues around the simple combination of data from the diverse and disparate sources collated here. The datasets are available on the Cefas Data Hub (https://www.cefas.co.uk/cefas-data-hub/). The referenced data sources are listed in Sect. 5.
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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,008 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».