Report of the Workshop on Real-time Coastal Observing Systems for Ecosystem Dynamics and Harmful Algal Blooms (WKHABWATCH)
Bibliographic record
Abstract
A Workshop on Real-time Coastal Observing Systems for Ecosystem Dynamics and Harmful Algal Blooms [WKHABWATCH] (Co-Chairs: M. Babin, France and J. Cullen, Canada) was held in Villefranche-sur-Mer, France from 11–21 June 2003. There is a great deal of interest, throughout the world, in the installation of ocean observation systems to provide the data and knowledge needed to detect and forecast physical, chemical and biological changes in coastal and open-ocean ecosystems. Recent advances in instrumentation, communications and modelling capabilities have led to the design of prototype real-time observation and prediction systems for coastal ecosystems. Important phenomena in coastal waters include flooding and coastal erosion, oxygen depletion due to eutrophication, and harmful algal blooms (HABs). However, many of the new approaches are unfamiliar to potential users. Optical and chemical sensors are, for instance, increasingly used from various platforms. Effective use of these sensors does not necessarily require advanced technical training, but it does require knowledge of the underlying theoretical and technical principles, how to properly deploy these instruments, methods for processing data, approaches for interpreting the results within reasonable limits, and how such results can be incorporated into different kinds of predictive models. It is for this reason that the “Workshop on Real-time Coastal Observing Systems for Ecosystem Dynamics and Harmful Algal Blooms” was convened at the Observatoire Océanologique de Villefranche and Citadelle of Villefranche-sur-Mer, France. The idea of this workshop initially emerged from the Working Group on Harmful Algal Blooms Dynamics (WGHABD) of ICES; the first stages of planning at a meeting in Dublin were critical. Major support from the European Commission made it possible for planning to proceed.
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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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".