Surface Microplankton Composition at a Hyper Saline Oligotrophic Environment of Bitter Lake on the Suez Canal, Egypt
Bibliographic record
Abstract
The Bitter Lake is the central and most important water body of the Suez Canal as it contains 85% of the water of the canal system. This study reports the microplankton found occurring in the surface water of the Bitter Lake at monthly intervals from November 2008 until November 2009. A total of 130 taxa were identified, among which 67 taxa were of Bacillariophyceae, 15 Dinophyceae, 11 Chlorophyceae, 11 Cyanophyceae, 1 Euglenophyceae, 18 Tintinnidae, 4 Foraminiferidae, as well as 3 of Rotifera. Species diversity, numerical abundances and dynamics were analyzed for each taxon at three sites inside the Bitter Lake. At each of these sites Bacillariophyceae were predominant in the standing crop forming 67.2% of the total microplankton community with an average of 11,594 ind. L -1 . The Dinophyceae occupied the second rank constituting about 16.5% of the total microplankton. Increase of microplankton abundance started in spring with maximum values being attained in late summer and early autumn (August ), with an average of 37,498 ind. L -1 , while January was characterized by the lowest density (9,251 ind. L -1 ). Relatively higher diversity values were recorded at the northern part of the lake and a progressive decline in diversity was observed southward. Nutrient concentrations in the lake waters were very low, with silicate varying between 0.52-1.34 µM, phosphate between 0.14 and 0.55 µM and nitrate between 0.82-3.16 µmol L -1 . Moreover, chlorophyll a fluctuated between 0.4 and 0.89 µg L -1 . Data from microplankton analyses, nutrient (P) and chlorophyll a concentrations and transparency measurements were used to assess the ecosystem health of the Bitter Lake according to OECD, Canadian, and Quebec classification criteria, and it is concluded that the Bitter Lake be classified as an ultraoligotrophic lake.
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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 | 0.001 | 0.001 |
| 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".