Estimating Time Series Phytoplankton Carbon Biomass: Inter-Lab Comparison Of Species Identification And Comparison Of Volume-To-Carbon Scaling Ratios
Bibliographic record
Abstract
Abstract An inter-calibration exercise was conducted to assess the performance of six phytoplankton taxonomists working within the Danish National Aquatic Monitoring and Assessment Program (DNAMAP). For species abundance and cell volume, a 2-fold difference was found among different estimates for subsamples from the same sample, which in turn cascaded into large differences in the species-specific carbon biomass contribution. The mean total carbon biomass estimated showed high variability (CV 43%) among the six taxonomists, but large variations were present within results produced by individual taxonomists (CV 8e50%), and one of the taxonomists produced significantly lower estimates than the others. Using data from phytoplankton time series samples, we also assessed the effect using a table of species-specific cell volumes versus cell volume measurements from a sample on carbon biomass values. For an example, the older cell-volume-to-carbon conversion method with fixed carbon-conversion constants was compared to the more recent approach of scaling biovolume to carbon biomass based on established regressions. We found that the regression between community biomass estimated by the old method versus the more recent equation yielded a slope close to 1, thus indicating general similar community biomass estimated between the methods. Type II regression suggested a high degree of variability in the estimates (17%). The highest degree of uncertainty was found by type II linear regression, when we compared the community biomass of diatoms estimated by cell sizes measured by sample to diatom community biomass estimated from cell sizes from a table of fixed cell sizes. In this analysis variation among methods for carbon estimation of individual samples was as high as 114%. Therefore, we recommend that, particularly for diatoms, cell volumes should be determined from the sample, or that table values be based on monthly estimates for at least the dominant diatom species for each study area.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".