Bibliographic record
Abstract
Increased harmful algal blooms by cyanobacteria are threatening public health and limiting human activities related with freshwater ecosystems. Phosphorus (P) has long been suggested as a critical nutrient for cyanobacterial bloom through field research in Canada during the 1970s, proposing a P-based freshwater management guideline. However, recently, nitrogen (N) has also been highlighted as an impacting nutrient on cyanobacterial harmful algal blooms (CyanoHABs). Due to the intensive and frequent observation of Microcystis, this kind of paradigm shift from P limitation to season-dependent N or P limitation has an important implication for a dual nutrient management strategy in eutrophic freshwaters. Through recent international researches, general strategies to control CyanoHABs in lakes and reservoirs are as follows: a dual nutrient (N & P) reduction, wastewater collection and treatment, pre-treatment of influent water in buffer zones, dredging of sediment, reduction of residence time, algal collection, and precipitation and flocculation of cyanobacteria. In addition, sustainable and integrative freshwater algae management should be carried out, based on the ecological aspect, because cyanobacteria are not the target organism to be eradicated, but an essential microbial member in the freshwater ecosystem.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".