Modeling the growth dynamics of <em>Cladophora</em> in eastern Lake Erie
Bibliographic record
Abstract
Cladophora glomerata is a filamentous green alga that currently forms extensive blooms in nearshore areas of Lake Ontario, eastern Lake Erie, Lake Michigan, and isolated locations in Lake Huron. The biomass, areal coverage, algal bed characteristics, and tissue phosphorus concentrations of Cladophora glomerata were measured at 24 nearshore rocky sites along the northern shoreline of Lake Erie?s eastern basin between 1995-2002. Midsummer areal coverage at shallow depths (≤5m) ranged from 4-100 %, with a median value of 96%. Peak seasonal biomass ranged from <1 to 940 g m-2 dry mass (DM), with a median value of 171 g m-2 DM. Tissue phosphorus varied seasonally, with initial high values in early May (0. 15 to 0. 27 % DM; median 0. 23 % DM) to midsummer seasonal low values during peak biomass (0. 03 to 0. 23 % DM; median 0. 06 % DM). A numerical Cladophora growth model (CGM) was revised and field-tested at 5 sites in eastern Lake Erie during 2002. The CGM is useful for: 1) Predicting Cladophora growth, biomass, and tissue phosphorus concentrations under non-point source P loading with no depth restrictions; 2) providing estimates of the timing and magnitude of the midsummer sloughing phenomenon; 3) determining the contribution of Dreissena invasion to the resurgence of Cladophora in eastern Lake Erie; and 4) developing management strategies for Cladophora abatement. The CGM was applied to investigate how the spatial and temporal patterns of Cladophora growth were influenced by the natural variability in environmental parameters in eastern Lake Erie. Seasonal patterns in Cladophora growth were strongly influenced by temperature, and peak depth-integrated biomass was strongly influenced by both available light and phosphorus. The photosynthetic capacity of field collected Cladophora was a poor predictor of the mid-summer sloughing phenomenon. The CGM, however, predicted that self-shading within the dense Cladophora mats would have caused negative growth rates at the base of the dense mats for 14 days prior to the sloughing event. The metabolic imbalances at the base of the Cladophora mats were driven primarily by the availability of light and were exacerbated by intermediate water temperatures (~23°C). The excellent agreement between model simulations and field data illustrates the ability of the CGM to predict tissue P and growth over a range of sites and depths in eastern Lake Erie and suggests potential for the model to be successfully applied in other systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".