Global Evaluation of the Impacts of Storms on freshwater Habitat and structure of phytoplankton Assemblages
Bibliographic record
Abstract
Phytoplankton abundance and composition are sensitive to water column conditions which are strongly influenced by weather (e.g., wind and rain) and climate change. The FRB-Cesab GEISHA project was framed in support to the GLEON Stormblitz project to gather and analyse time-series through collaborative efforts to assess the impacts of storms on phytoplankton. The project includes more than 80 researchers from governmental institutes and universities. GEISHA was, among others, able to: gather and standardize existing long-term datasets, assess the impact of storms on nutrients, light, water column stability and subsequent impacts on the structure of phytoplankton communities, perform meta-analyses to evaluate the sensitivity of aquatic ecosystems to extreme weather events, and highlight that biological consequences of storms on phytoplankton are fundamental to the dynamics of lakes and yet are still poorly understood. There is a real need for scientific collaboration to understand the impact of extreme weather events on lakes. This document summarizes, in just a few pages, the project’s context and objectives, the methods and approaches used, the main findings, and the implications for science, society, and both public and private decision-making. _________________________________________________________________________ L’abondance et la composition du phytoplancton sont sensibles aux conditions de la colonnes d’eau qui elles-mêmes sont fortement influencées par la météorologie (ex. le vent et la pluie) et le changement climatique. Imaginé dans le cadre du projet « Stormblitz » du GLEON, le projet FRB-Cesab Geisha a pour objectif d’évaluer les impacts des tempêtes sur les communautés phytoplanctoniques. Geisha a été conçu pour permettre de rassembler et d’analyser des séries chronologiques par le biais d’un effort collaboratif au niveau international. Le projet regroupe plus de 80 chercheurs d’instituts gouvernementales et d’universités. Geisha a notamment permis : de rassembler et standardiser les jeux de données existants, d’évaluer l’impact des tempêtes sur les nutriments, la lumière, la stabilité de la colonne d’eau et les conséquences sur la structure des communautés phytoplanctoniques, d’effectuer des méta-analyses pour évaluer la sensibilité de ces écosystèmes et, de mettre en en évidence que les conséquences biologiques des tempêtes sur le phytoplancton sont fondamentales dans la dynamique des lacs et sont pourtant encore mal comprises. Il y a un réel besoin de collaboration scientifique pour comprendre l’impact des phénomènes météorologiques extrêmes sur les lacs. Ce document synthétise en quelques pages le contexte et les objectifs du groupe, les méthodes et approches utilisées, les principales conclusions ainsi que l'impact pour la science, la société, la décision publique et privée.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".