Asking the Right Questions about Nutrient Control in Aquatic Ecosystems
Bibliographic record
Abstract
Eutrophication remains the greatest stressor affecting fresh-water ecosystems across North America and Europe. However, over the past five years, the scientific literature has seen a resurrection of the debate over causes of eutrophication,1,2 with resulting confusion in management circles. This debate has been poorly defined, fractious, and oftentimes counterproductive. Eutrophication has been defined as the process leading to increased algal productivity in lakes through time. It can be a natural process, as lakes age; or, in the case of cultural eutro-phication, it can be facilitated by anthropogenic nutrient inputs. Management concern about eutrophication centers on a change from a desirableoften clear water state, to an undesirable state, exemplified by issues of low hypolimnetic dissolved oxygen, nuisance algal productivity, and in many cases, production of algal toxins. Unfortunately, this may not be a linear change through time, but instead can occur as a state shift or regime change3.
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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".