EFFECT OF IN SITU EXPERIMENTAL SHADING ON THE PHOTOSYNTHESIS OF CANADIAN WATERWEED (ELODEA CANADENSIS) FROM SONGKHLA LAGOON, THAILAND
Bibliographic record
Abstract
Macrophytes play an important role in providing habitat structure, nutrient cycling and improvement of water quality in freshwater ecosystems.However, increasing human population and the industrial revolution during past several centuries have increased the utilization of natural resources and have caused strong changes in the structure and function of the environment, such as high sedimentation that decreases light penetration.This study investigated the effects of in situ experimental shading on photosynthetic performance of submerged Elodea canadensis macrophytes and identified the level of light that is critical for growth and photosynthesis of this species.Photosynthetic performance, chlorophyll a and b concentrations, organic and carbon contents, percentage cover, and morphology were estimated in E. canadensis from middle of Songkhla lagoon under 4 treatments (25, 50, 75, and 100% of natural light) for 10 weeks.The results show that there were no differences in growth and photosynthesis among the treatments, but low light led to changes in chlorophyll concentration, Fv/Fm, and Ik, suggesting adaptations to a low light regime.This study provides an understanding of physiological tolerance and response to shading and shows how species of aquatic macrophytes respond to future climatic and anthropogenic changes, thereby supporting development of sustainable lagoon management plans.
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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 | 0.001 | 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) | 0.000 | 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".