Theme Session E_What can we learn from long-term time-series?
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.136 | 0.050 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".