Optimization of variable fluorescence measurements of phytoplankton communities with cyanobacteria
Bibliographic record
Abstract
Excitation-emission fluorescence matrices of phytoplankton communities were simulated from laboratory-grown algae and cyanobacteria cultures, to define the optical configurations of theoretical fluorometers that either minimize or maximize the representation of these phytoplankton groups in community variable fluorescence measurements. Excitation sources that match the photosystem II (PSII) action spectrum of cyanobacteria do not necessarily lead to equal representation of cyanobacteria in community fluorescence. In communities with an equal share of algae and cyanobacteria, inducible PSII fluorescence in algae can be retrieved from community fluorescence under blue excitation (450-470 nm) with high accuracy (R (2) = 1.00). The highest correlation between community and cyanobacterial variable fluorescence is obtained under orange-red excitation in the 590-650 nm range (R (2) = 0.54). Gaussian band decomposition reveals that in the presence of cyanobacteria, the emission detection slit must be narrow (up to 10 nm) and centred on PSII chlorophyll-a emission (~683 nm) to avoid severe dampening of the signal by weakly variable phycobilisomal fluorescence and non-variable photosystem I fluorescence. When these optimizations of the optical configuration of the fluorometer are followed, both cyanobacterial and algal cultures in nutrient replete exponential growth exhibit values of the maximum quantum yield of charge separation in PSII in the range of 0.65-0.7.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".