Theme Session E_What can we learn from long-term time-series?
Notice bibliographique
Résumé
Book of abstracts of theme session E:What can we learn from long-term time-series?Conveners: Dafne Eerkes-Medrano (UK), Lidia Yebra (Spain), Frédéric Cyr (Canada), Dave Clarke (Ireland)Longterm climatic changes in a tidal inlet: causes and effectsIntegrating long-term survey data and environmental variables to improve Northern shrimp stock assessment in the Barents SeaUnravelling the Past: A Historical Perspective on Cormorant Diets and Cod Predation in the Changing Western Baltic ecosystemLong-term time-series of plankton as a tool for MSFD implementationAssessing the dynamic nature of risk in North Sea fish populations using long-term time-series and the ecorisk R packageRediscovering lost herring spawning grounds: unearthing historical records overlooked by modern scienceThe IOC Harmful Algal Information System – The value of a global long term meta databaseCoastal lagoon management based on multidisciplinary time-series: the case of the Mar Menor Catch me if you can: Spatiotemporal changes in pelagic recreational fishes determined from long-term catch (MRIP) dataShort-term variability but long-term consistency in diets of dolphinfish Coryphaena hippurus revealed by multi-decadal sampling of a sportfishing tournament in the western North AtlanticTrends of some key deep fish species of northern the Alboran Sea: Insights from 30 years of MEDITS SurveysSeasonal, inter-annual and gender specific variations of life history traits of Sardina pilchardus in the Southern Alboran SeaTime Series Analysis of Genetic Variation in Atlantic Herring: Responses to Climate Variability and Fishing Pressure Over a CenturyBlue Whiting in Icelandic Waters: Migration, Residency, and Population ConnectivityA half-century of shellfish paralytic shellfish toxicity (PST) data analyzed with a simple severity index to reveal long-term cycles and trends Decadal Patterns in Fish Taxonomic and Functional Richness in Swedish Coastal WatersToward accurate and reliable time-series monitoring of zooplankton diversity using DNA metabarcoding: MetaZooGene Intercalibration Experiment (MZG-ICE) Unveiling Long-Term Trends in Benthic-Demersal Food Web Functioning: A 30-Year Perspective from the Cantabrian Sea Responses of fish community to ocean warming in the Bay of BiscayInforming European eel conservation using a long-term data series from a Swedish river system What’s on the menu? Combining long-term fixed time series and spatially extensive survey of copepod abundances across English Channel and Southern North Sea to map out herring larvae prey fieldsDecadal Zooplankton Variability and Climate Forcing in the Northeast U.S. Shelf Large research vessels are essential to marine ecosystem observations and ocean sustainabilityInvestigating climate change impacts on phytoplankton communities through long term monitoringTracing the Time-Resolved pCO₂ Flux in the Algerian Basin Changes in condition of Salmon (Salmo salar) sampled in the southern Baltic SeaThe value of benthic long-term series: compilation of science to support management decisionsImpacts of Changing Survey Diel Protocols in Estimating Diversity and Abundance Indices Identifying the influence of geological processes on properties of the World Ocean through long-term oceanic data series Sporadic recruitment of the bluemouth rockfish in the North Sea in relation to Atlantic inflowDiverse time-series data have supported management of Bermuda’s fisheriesThe Integration of Metabarcoding and Morphological Techniques in Long-Term Plankton MonitoringTime-series ecosystem monitoring of the NW Atlantic continental shelf: Metabarcoding analysis of zooplankton diversity and climate-driven range shiftsEnvironmental drivers of spring spawning herring individual growth in the Gulf of Riga, Baltic Sea, 1961-2020 Offshore wind farms leave ecological footprints on soft sediment fish and epibenthic communities in between the turbinesA machine-learning based, shellfish biotoxin forecasting method: successes from Maine, USA and opportunities for other regionsA machine-learning based, shellfish biotoxin forecasting method: successes from Maine, USA and opportunities for other regions
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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,007 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».