Improved estimates of phytoplankton community composition based on in situ spectral fluorescence: use of ordination and field-derived norm spectra for the bbe FluoroProbe
Bibliographic record
Abstract
The use of spectral fluorometers for assessing phytoplankton concentrations and taxonomic composition in aquatic environments is increasingly common. However, the accuracy of such assessments suffers because the necessary norm spectra (spectral fingerprints) are derived using selected taxa and laboratory conditions that may not adequately represent the taxa and environmental conditions in the study area. Ordination analysis of raw fluorescence data has been proposed as a better means of interpreting spectral fluorescence data. We applied nonmetric multidimensional scaling and cluster analysis to raw in situ fluorescence data from Sturgeon Bay, a small, mesotrophic embayment of Georgian Bay (Lake Huron) to obtain system-specific norm spectra for the bbe FluoroProbe. The revised spectra gave improved estimates of phytoplankton taxonomy (root mean square error of 10% versus 14%) and of dissolved organic carbon and chlorophyll a concentrations. While promising, this method should be further explored in other systems with different and (or) weaker gradients in phytoplankton biomass and taxonomic composition.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| 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".