Chlorophyll Mapping using MODIS/MERIS imagery over Case 2 Waters, Lake Winnipeg
Bibliographic record
Abstract
This report describes data and data collection procedures, data analysis and results of analysis undertaken in order to test and refine algorithms for use in mapping chlorophyll concentrations in Case 2 waters in Lake Winnipeg using MODIS and MERIS satellite data. Sampling described in this report was done by personnel from the Canadian Department of Fisheries and Oceans and from the Centre for Earth Observations Science in the Department of Geography, University of Manitoba. Core funding was through a Canadian Space Agency project grant. Water quality data were collected on whole- lake missions in May/June, July/August and September/October in 2002, 2003, and 2004. Simultaneous spectral remote sensing reflectance (RRS) data were collected off the ship’s bow for use in reflectance-chlorophyll algorithm development. Water samples were analysed for chlorophyll, total suspended solids, tripton and dissolved organic carbon concentration. Paired data collected in 2002 and 2003 were used to test relationships between reflectance and chlorophyll, and to develop regressions predicting chlorophyll in the water column. The regressions were validated using an independent data set collected in 2004. Remote sensing reflectance ratios explain about 60% of the variance in chlorophyll in Lake Winnipeg, and predicts chlorophyll with a standard error of about 0. .6 ln(ug.L-1). We used the ratio RRS531/RRS551 to map chlorophyll seasonally in Lake Winnipeg. The resulting maps compare favourably with maps created by interpolation of data collected on three whole- lake cruises.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".