Statistical trend analysis and classification of Lake Erie with size-fractionated primary production changes
Bibliographic record
Abstract
The article uses exploratory and change-point methods to investigate changes in the level and spatial pattern of size-fractionated primary productivity in Lake Erie during the summers of 1992 and 1996. In July 1992 and 1996 primary productivity measurements were made at 44 and 34 sampling stations, respectively, and separated into three size classes (<2 μm, 2 to 20 μm, >20 μm). Spatially, the overall productivity increased gradually from east to west, with the medium size class showing the highest rate of increase. The 1996 productivity was higher than that of the 1992 in almost all size classes. The 1996 level appears to be nearly proportional to the 1992 level for the medium and larger size classes. For the small size class, the increase occurs only in the western region of the Lake. These findings were supported graphically and by statistical modelling. Using geographical coordinates of sampling locations as explanatory variables, change-point analysis is used to separate the lake into regions such that each region has its own regression regime. The findings indicate that the characteristics of the east basin extend beyond its traditional physical boundaries and into the central basin. This analysis provides a more accurate characterization of the lake than the traditional practice of assuming that lake is divided into three homogeneous basins. Here the lake is divided into regions where the concentration within each region is allowed to vary but according to its own regression model.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".