New Records for the Turkish Freshwater Algal Flora in Twenty Five River Basins of Turkey, Part IV: Ochrophyta
Bibliographic record
Abstract
Turkish lakes have different morphometric and hydrological features as a result of different climates and noticeable altitude differences in Turkey that are necessary conditions to occur different habitats for algal diversity. The total number of algae taxa in the flora of Turkey has increased due to the growing number of studies on phytoplankton taxonomy and ecology in the last 40 years. This study aims to describe new planktonic algal taxa for the Turkish freshwater algal flora. A total of 56 Ochrophyta taxa were determined in this study, conducted from 2017 to 2019 in lakes lies in 25 river basins of Turkey. In 275 lakes, samples of phytoplankton were collected with water samplers from three depths (surface, middle, and bottom) of the euphotic zone, and then subsamples were obtained by mixing the water samples taken from these three depths. The plankton net with a pore diameter of 50 μm was also used for collecting samples of phytoplankton. The algal taxa was identified by using different types of compound and inverted microscopes in many laboratories. 30 Ochrophyta taxa of which were determined in this study, were reported as a new record for the first time for the freshwater algal flora of Turkey.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".