Report of the ICES/IOC Workshop on New and Classic Techniques for the Determination of Numerical Abundance and Biovolume of HAB-Species - Evaluation of the Cost, Time-Efficiency and Intercalibration Methods (WKNCT)
Bibliographic record
Abstract
A workshop with the aim to compare classical and molecular biological techniques for quantitative phytoplankton analysis took place at Kristineberg Marine Research Station, Fiskebäckskil, Sweden, 22–26 August 2005. A total of 24 participants (appendix 1) from ten countries participated in the workshop and 15 different techniques were compared. After advice from the Scientific Steering Committee (SSC, see Annex 2) and the ICES/IOC Working Group on Harmful Algal Bloom Dynamics (WGHABD) it was decided to focus on essentially one species, Alexandrium fundyense. This thecate dinoflagellate A. fundyense, strain CA 28, was maintained in unialgal cultures and used during the workshop in four different experiments. Experiment 1 was aimed at determining in which range of cell densities the methods are applicable to. Experiment 2 was designed to test the species specificity of the methods by adding a related species, Alexandrium ostenfeldii, to samples already containing A. fundyense. Experiment 3 tested the ability of the methods to detect the target organism, A. fundyense, which was added to a natural phytoplankton community from the Gullmar Fjord. A range of biomasses of natural phytoplankton was tested. Experiment 4 also tested the methods ability to enumerate A. fundyense in field samples. It was similar to Experiment 1, although, higher concentrations of the target species with background levels of other phytoplankton species were present in the samples distributed. The detailed results from the workshop will be presented elsewhere. In this report only an overview is presented. To summarise, the classical Utermöhl sedimentation chamber technique performed very well with similar results reported by the 2 participants who used different settling volumes to test this method. This method, however, was not as successful when the target organism A. fundyense was present in samples containing the morphologically similar species, A. ostendfeldii. In the experiment where discrimination with similar species was tested it appears that some of the A. ostenfeldii cells may have been misidentified as A. fundyense. The filtering techniques also produced good results but some tended to report lower cell numbers then the Utermöhl method. The filtering and calcofluor staining techniques performed well in the experiment that required the discrimination between A. ostenfeldii and A. fundyense. Sedgewick-Rafter and Palmer-Maloney chambers did not appear to work well when target cell range was between ~ 500–5 000 cells per Litre. These methods improved when cell concentrations increased to ranges between 25 000–100 000 cells per Litre. The Haemocytometer method was unsuccessful at recording the target cell numbers in question when compared to the other methods tested. This method is considered a quick and easy method for culture studies and during bloom situations when cell densities are exceptionally high. The Quantitative PCR method did not perform as well as expected during the workshop. It is thought that a more thorough calibration of this method would have given closer results to those reported by the other methods. The whole cell hybridisation assays with fluorescence microscope detection produced reasonable results, although cell numbers were often underestimated similar to the filtration methods above. These methods all used filtration to concentrate the sample. The whole cell assay with ChemScan detection both over- and underestimated the cell numbers compared to other techniques. The sandwich hybridisation assay with colourimetric detection produced good results although cell numbers were often underestimated. Only a few samples were processed using the hybridisation assay with microarray fluorescent detection and the sandwhich hybridisation assay using electrochemical detection because of technical problems during the workshop. These methods are considered to be still at a development stage. After the workshop preserved samples were transported to Canada for analysis using a type of advanced particle counter called the FlowCam. Although only a subset of samples were analysed the results reported are quite good.
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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 | 0.046 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".