Understanding and managing the re-eutrophication of Lake Erie: Knowledge gaps and research priorities
Bibliographic record
Abstract
Eutrophication of freshwaters is already a problem in many regions globally and will probably worsen as human populations grow and consume more resources. The ability of researchers and governments to anticipate, mitigate, and restore eutrophic freshwaters in a cohesive, integrated manner suffers from key uncertainties in our understanding of the watershed-to-lake continuum. Here, we use Lake Erie and its watershed as an example of a system in which there is a pressing need to resolve these uncertainties. In recent history, Lake Erie both suffered and recovered from serious eutrophication and related issues. More recently, however, there has been a resurgence of eutrophication and associated harmful algal blooms in Lake Erie, with symptoms reminiscent of prior eutrophication. This resurgence has led the USA and Canadian governments to commit to substantially reducing P inputs into Lake Erie in an effort to control eutrophication. We illustrate how key uncertainties about Lake Erie and its watershed contribute to challenges we face in restoring this ecosystem and propose avenues for their resolution. To this end, we contend that an ecosystem approach will be required for managing the eutrophication of freshwaters.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".