State-of-the-art and recent progress in phytoplankton succession modelling
Bibliographic record
Abstract
Dynamic phytoplankton succession models are an essential instrument to improve scientific knowledge on the development of algal blooms characterized by a specific composition and to support water quality management decisions. The peculiar structure and formulation of these models generate questions that differ from the ones found in modelling eutrophication and are related to simulation of multiple phytoplankton groups. In this work, a classification of phytoplankton models simulating several algal groups is provided. Coupled succession models, explicitly describing nonlinear interactions between physical and biological processes and capturing the response of phytoplankton community to environmental changes, are analyzed in detail. Approaches, actual achievements, and developments of succession models are examined. In particular, we discuss the level of discrimination adopted, number and type of algal groups simulated, biomass unit employed, type of model evaluation used, and efficacy of prediction achieved. Simulations of multiple phytoplankton group behaviour still produce significant deviations over time or in magnitude compared to the patterns observed. Frequently, goodness-of-fit estimation is only graphical and statistics adopted do not allow a direct comparison between different models. To facilitate comparisons we propose the use of a common statistic that would be applied, separately, to all the phytoplankton groups differentiated in each model. Each model’s level of complexity in relation to prediction ability is also analyzed. Through this work we aspire to orient upcoming works and encourage others to apply mechanistic succession models, including the description of physical and biological relationships, specific phytoplankton behaviour and interactions between phytoplankton groups.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".