Harmonizing science and management options to reduce risks of cyanobacteria
Bibliographic record
Abstract
Management of cyanobacteria has become an increasingly complex venture. Cyanobacteria risks have amplified as society moves forward in an era of accelerated global changes. The cyanobacteria management "pendulum" has progressively shifted from prevention to mitigation, with management considerations often put forth after bloom formation. A universal system (i.e., one-size-fits-all management) fails to provide a management path forward due to the inherent complexities of each lake. A tailored management plan is needed: the right species at the right time in the right place (i.e., the three Rs). The three Rs represent a customizable management strategy that is flexible and informed by advances in scientific understanding to lower cyanobacteria-associated risks. Identifying thresholds in risk tolerance, where thresholds are defined by community collectives, is essential to frame cyanobacteria management targets and to decide on what management interventions are warranted.
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 | 0.039 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".