Identification of phytoplankton isolates from the eastern Canadian waters using long-read sequencing
Bibliographic record
Abstract
Abstract As important primary producers and key components of biogeochemical processes, phytoplankton communities are highly diverse and continually adapt to changes in the environment, impacting the entire marine ecosystem. Consequently, it remains important to isolate, culture and properly describe new phytoplankton strains to provide relevant model organisms for laboratory research and accurate reference sequences for identification. Here, 73 phytoplankton strains from the eastern Canadian waters were isolated and genetically characterized using a long rRNA fragment (~4000 bp) covering the 18S, ITS and 28S rRNA regions generated with long-read sequencing technology. While most strains (66%) were accurately identified using the partial 18S rRNA gene (~1200 bp—V4–V9), this study broadens the current 28S rRNA reference database by incorporating 41 distinct strains including 30 unique species. In addition, previously unpublished long-read reference sequences were generated for a few genera, including Picochlorum and Droopiella. Species that were previously poorly described in the eastern Canadian waters were also genetically characterized, including a Chaetoceros similis strain. Overall, this work expands the total number of long-read references, an essential resource for accurate identification of phytoplankton and environmental sequences, thereby advancing the taxonomic resolution that could lead to a better understanding of environmental microbial diversity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".