The development of a planktonic index of biotic integrity for Lake Erie
Bibliographic record
Abstract
Herein, I provide a water quality monitoring tool for the offshore waters of Lake Erie.The Planktonic Index of Biotic Integrity (P-IBI) was developed using Lake Erie zooplankton and phytoplankton data.I reviewed literature related to Indices of Biotic Integrity and plankton ecology (Chapter 1).To construct a valid IBI, I conducted temporal and spatial analyses for different parameters of Lake Erie plankton and plankton sampling regimens (Chapter 2).Using this information, I then determined appropriate temporal and spatial sampling frequency needed for the P-IBI.Further, using Lake Erie monitoring data I provide information on the invasive predatory cladoceran, Cercopagis pengoi (Chapter 3), and on the temporal and spatial distribution of Limnocalanus macrurus (Chapter 4), a pollution intolerant calanoid copepod.The P-IBI was developed using phytoplankton and zooplankton data from 1970 and 1996, validated with respect to total phosphorus and chlorophyll a concentrations, and applied to approximately 10 years of Lake Erie plankton data (Chapter 5).P-IBI candidate zooplankton metrics included an abundance ratio of calanoid copepods to cladocerans and cyclopoid copepods, percentage density of large Daphnia, rotifer composition, density of the calanoid copepod Limnocalanus macrurus, percentage biomass of the predatory invasive zooplankters Bythotrephes and Cercopagis, biomass of crustacean zooplankton/ biomass of phytoplankton, and biomass of crustacean zooplankton.Candidate phytoplankton metrics included a generic index of diatoms, abundance ratio of centric diatoms to iii pennate diatoms, biomass of inedible algae taxa, percentage biomass of bluegreen algae of the total phytoplankton biomass, percentage biomass of the potentially toxic Microcystis, Anabaena, and Aphanizomenon of total phytoplankton biomass, and biomass of edible algae taxa.Discriminant analysis was used to determine which metrics reflected levels of degradation in Lake Erie, as based on total phosphorus and chlorophyll a concentrations.A Kappa statistic was calculated to determine the classification accuracy of the significant metrics obtained from the discriminant analysis.Significant metrics for June included biomass of edible algae taxa, percentage Microcystis, Anabaena, and Aphanizomenon of total phytoplankton biomass, and an abundance ratio of calanoids to cladocerans and cyclopoids.The only significant metric for July was the density of Limnocalanus macrurus, while both the zooplankton abundance ratio and crustacean zooplankton biomass were significant metrics for August.The P-IBI was significantly correlated with a measure of eutrophication (e.g., total phytoplankton biomass) and showed an increase of water quality between 1970 and the mid-1990's (1996 and 1997) in Lake Erie, with declining water quality in the late 1990's and early 2000's.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".