Seasonal dynamics of plankton communities coupled with environmental factors in a semi arid area: Sidi Sad reservoir (Center of Tunisia)
Bibliographic record
Abstract
In this study, we studied the influence of the physical-chemical and biological factors (bacterioplankton and phytoplankton abundances) for zooplankton dynamics in a Sidi Saâd reservoir in Centre of Tunisia.The samplings were carried out in spring, summer, autumn and winter (2005 to 2006) in the deepest station (surface, 5, 10 and 15 m).In this reservoir, the highest density of zooplankton abundance was recorded in summer (92.2 ind L -1 ; 0.43 × 10 3 µg L -1 ) and autumn (86.9 ind L -1 ; 0.23 × 10 3 µg L -1) at a depth of 10 m.The copepods (56% of total zooplankton abundance) and cladocera (42% of total zooplankton abundance) were the most abundant groups.The physico-chemical factors, especially the water temperature (r = 0.53, p = 0.027, n = 16) and dissolved oxygen (r = -0.59,p = 0.03, n = 16), influence directly the zooplankton community.It was suggested that in Sidi Saâd reservoir, both the top-down and bottom-up regulations account for the regulation of zooplankton.The phytoplankton was the factor responsible for the structure and seasonal dynamics of the zooplankton community, which are well related to changes in algae diversity and abundance, noting that cyanobacteria have major impacts.The "top-down" effect of planktivorous fish on the zooplankton is a significant factor affecting the plankton community's dynamics in this reservoir.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".