The use of the Laser Optical Plankton Counter to measure zooplankton size, abundance, and biomass in small freshwater lakes
Bibliographic record
Abstract
The Optical Plankton Counter (OPC) has been used in a variety of environments since its introduction over decade ago, but its use in freshwater lakes has been limited by high densities of zooplankton and detritus. The newer Laser Optical Plankton Counter (LOPC) has several modifications from its predecessor, and the goal of this study was to examine whether it could be used to measure average size (µm equivalent spherical diameter, ESD), abundance (particles L−1), and biomass (µg dry weight L−1) of zooplankton in samples from 18 lakes in the Eastern Townships region of Quebec, Canada. The LOPC slightly overestimated the size of copepods, and consistently underestimated Daphnia by approximately 25% ESD. Densities and biomass of net samples were very similar between the LOPC lab version and traditional microscope analyses suggesting that the LOPC can be reliably used to process preserved net samples. When the LOPC was towed in situ vertically in Lake Memphremagog, QC, Canada, estimated zooplankton abundances were ten times net sample values from the same water column, but similar abundances were found between the LOPC and pumped zooplankton samples at 2 m depth. These results indicate that the LOPC may be well suited for analyses of zooplankton abundance and biomass in productive freshwater lakes.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".