A multivariate approach for evaluating progress towards phytoplankton community restoration targets: Examples from eutrophication and acidification case histories
Bibliographic record
Abstract
Two disturbed aquatic systems, Bowland Lake, a strongly acidic lake (pH=4.9) in south-central Ontario, and the eutrophic Bay of Quinte at the northeastern end of Lake Ontario were selected as case histories for demonstration of an analytical protocol for defining and evaluating phytoplankton community rehabilitation targets. Bowland Lake was experimentally neutralized in 1983 to evaluate the efficacy of an application of a powdered limestone slurry for rehabilitating biological communities in the lake. The Bay of Quinte was identified by the International Joint Commission in 1985 as one of 42 ‘Areas of Concern’ in American and Canadian waters of the Great Lakes for which a Remedial Action Plan is required to restore beneficial uses. Phosphorus loading controls were implemented in the Bay of Quinte drainage basin in late 1977 to early 1978. In both cases, pre- and post-treatment phytoplankton community structure data were used to assess the degree of phytoplankton community impairment and the response to remediation by comparing the phytoplankton communities to those from suitable reference locations. The rationale, development and application of model phytoplankton communities intended to serve as ‘targets’ for phytoplankton community rehabilitation are presented within a multivariate framework of community structure. It is recommended that these, or similar analytical protocols based on multivariate methods for defining community structure, be more widely applied when corrective actions are taken to rehabilitate aquatic systems.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".