Bibliographic record
Abstract
Cyanobacteria blooms represent a global problem with human health and economic and ecosystem nuisance effects. Until now, we have used arbitrary and sometimes confusing criteria to decide whether we are in the presence of a Cyanobacteria bloom. Through scientometric analysis of studies published between 1953 and 2022, I aimed to identify a threshold value that can be used to quantify Cyanobacteria blooms numerically. I considered several methodological approaches (field, field-experimental, and satellite data) and kinds of environments (coastal, marine, and continental ecosystems). The analysis revealed that 48% of the papers used “cyanobacteria bloom” or similar terms qualitatively without providing or employing a quantitative definition. These papers were focused on other topics related to the ecology of Cyanobacteria (78%), while some others (21%) defined blooms by using visual criteria (scum visualization or water discoloration). Of the other half (52%), a few were mathematical models or review papers (5%), while the rest (65%) defined blooms quantitatively. Of these, most reports (approximately 80%) were from eutrophic inland waters and reported mean values for blooms between 80 000 and 249 000 cells mL−1 or 41 and 69 µg L−1 of chlorophyll- a. Calculations were also carried out for oligotrophic to mesotrophic inland waters and marine-coastal waters, which had fewer reports available in the literature. This is a first attempt to identify a consensual definition based on values reported as blooms in nature, a threshold that can undoubtedly be useful in the future to make possible comparisons among several environments and temporal scales.
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 | 0.037 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.046 | 0.027 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".