Dynamics of the Lake Michigan food web, 19702000
Bibliographic record
Abstract
Herein, we document changes in the Lake Michigan food web between 1970 and 2000 and identify the factors responsible for these changes. Control of sea lamprey (Petromyzon marinus) and alewife (Alosa pseudoharengus) populations in Lake Michigan, beginning in the 1950s and 1960s, had profound effects on the food web. Recoveries of lake whitefish (Coregonus clupeaformis) and burbot (Lota lota) populations, as well as the buildup of salmonine populations, were attributable, at least in part, to sea lamprey control. Based on our analyses, predation by salmonines was primarily responsible for the reduction in alewife abundance during the 1970s and early 1980s. In turn, the decrease in alewife abundance likely contributed to recoveries of deepwater sculpin (Myoxocephalus thompsoni), yellow perch (Perca flavescens), and burbot populations during the 1970s and 1980s. Decrease in the abundance of all three dominant benthic macroinvertebrate groups, including Diporeia, oligochaetes, and sphaeriids, during the 1980s in nearshore waters ([Formula: see text]50 m deep) of Lake Michigan, was attributable to a decrease in primary production linked to a decline in phosphorus loadings. Continued decrease in Diporeia abundance during the 1990s was associated with the zebra mussel (Dreissena polymorpha) invasion, but specific mechanisms for zebra mussels affecting Diporeia abundance remain unidentified.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".