Using linear models with correlated errors to analyze changes in abundance of Lake Michigan fishes: 1973-1992
Bibliographic record
Abstract
We examined annual changes in relative abundance of Lake Michigan fishes using linear models with correlated errors in space and time. Abundance of bloater (Coregonus hoyi), deepwater sculpin (Myoxocephalus thompsoni), slimy sculpin (Cottus cognatus), alewife (Alosa pseudoharengus), and rainbow smelt (Osmerus mordax) was monitored with bottom trawls at 10 discrete depths (between 18 and 110 m) off eight fixed ports from 1973 to 1992. The model describing abundance included fixed effects of year, port, depth, and interaction terms as well as quadratic and cubic effects of year and depth because changes in abundance were not strictly linear. Observed temporal trends in abundance varied with species and depth. Additionally, trends in alewife and slimy sculpin abundances depended on port. Cubic trends in the abundance of bloater and quadratic trends in deepwater sculpin and rainbow smelt abundances were similar among ports, permitting lakewide inferences for these species. Mean bloater abundance was low throughout the 1970s, increased during the 1980s, and reached high levels by 1990. Mean abundances of deepwater sculpin and rainbow smelt increased from 1973 to the mid-1980s and declined thereafter. The linear model with correlated errors can be readily applied to repeated-measures data from other fixed-station fishery surveys and is appropriate for data exhibiting spatial and temporal autocorrelations.
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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".