Using remote sensing methods to determine the contribution of cyanobacteria to plankton biomass in Lake Winnipeg
Bibliographic record
Abstract
The object of this study has been to develop a remote sensing method to estimate the relative proportion of cyanobacteria in plankton populations in Lake Winnipeg. In previous studies, I tested various remote sensing algorithms for determining total chlorophyll biomass of the plankton population in the lake. These studies resulted in successful validation of the fluorescence line height (FLH) index as a measure of chlorophyll in Lake Winnipeg, using either MODIS or MERIS data recorded by American and European satellites respectively. These studies also suggested that precision of these algorithms would benefit from some ability to discrimination among phytoplankton taxa. Cyanobacteria, in particular, were shown to vary inversely with FLH, while other major communities varied positively. In this study, I show that a multiple regression on band ratios constructed from seven MERIS spectral bands explains 63% of the variance of cyanobacteria biomass expressed as a fraction of total biomass, and predicts the cyanobacteria fraction with a standard error of 0.15. This result is based on a multi-year, multi-season set of paired surface reflectance data paired with fluorescence determinations of the cyanobacteria fraction in plankton. I include in the report several maps of cyanobacteria and of total chlorophyll biomass and discuss the significance of coincident and differing patterns in the two map sets. Data collected in the course of this and prior optical work on Lake Winnipeg are stored on several DVDs accompanying this report. Data attached includes all analytical results currently available for water quality observations by the Department of Fisheries and Oceans on Lake Winnipeg from 2002-2007. Observations have been assigned geographical coordinates from the GPS record and files have been merged with algal taxonomic counts made by Algal Taxonomy and Ecology Inc. under contract to either the Department of Fisheries and Oceans or to the Centre for Earth Observations Science. Also filed on accompanying DVDs are data from all fluorescence measurements and estimates of major taxonomic distributions using Algal Online Analyzers on cruises of the Namao on Lake Winnipeg in 2003, 2004 and 2007, and surface spectral reflectance data recorded using field spectrometers in 2002, 2003 and 2004.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".