Size spectra analysis of a decade of Laurentian Great Lakes data
Bibliographic record
Abstract
Size spectra analysis (SSA) is used to detect changes in food webs by simplifying complex community structures through abundance-versus-biomass considerations. We applied SSA to 10 years (2006–2015) of data on Great Lakes organisms ranging in size from picoplankton to macrozooplankton. Summer pelagic size spectra slopes were near the theoretical value of −1.0, but spring slopes were steeper, reflecting seasonal differences in abundance of small and large individuals. Pelagic size spectra slopes were relatively stable over the time period we examined. Height (the predicted number of organisms at the spectra midpoint) varied among lakes and was slightly higher in summer than spring in more productive basins. Including benthic data led to shallower slopes when combined with pelagic data, suggesting benthic organisms may increase food web efficiency; height was less affected by benthic data. Benthic data are not routinely included in SSA, but our results suggest they affect slopes and therefore SSA-based predictions of fish abundance. The ability of SSA to track changes in trophic energy transfer makes it a valuable ecosystem monitoring tool.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".