Establishment of an early warning system for cyanobacteria using an online multi-probe system measuring physicochemical parameters, chlorophyll and phycocyanin
Bibliographic record
Abstract
Guidelines and recommendations for cyanobacteria and cyanotoxins in drinking water -- Cyberinfrastructure for environmental monitoring -- Project description -- Eutrophication and its consequences -- Occurrence of cyanobacteria blooms worldwide -- Cyanobacteria and cyanotoxins -- Triggers of cyanobacteria blooms -- Standards and recommendations for cyanobacteria monitoring -- Advantages and disadvantages of monitoring methods -- Fluorescence mechanism in cyanobacteria -- Studies using online in vivo fluorescence to estimate chl-a content of phytoplankton, including cyanobacteria -- Advantages and limitations of in vivo fluorescence -- Hypotheses and research objectives -- Site descriptions and sampling locations -- MPS specifications calibration, and validation -- Statistical analyses -- Laboratory and environmental validation of the in vivo PC fluorescence probe on the MPS -- Cyanobacterial monitoring at two drinking water treatment plants in Southern Quebec -- The integration of in vivo PC fluorescence into an existing cyanobacterial monitoring framework for the two monitored DWTPs.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".