Landscape pattern and fragment of sonneratia alba, avicennia alba and rhizophora apiculata in mangrove ecosystem as an effective ecological indicator tools for inorganic contaminants monitoring
Bibliographic record
Abstract
Mangrove forest ecosystems are threatened by direct impacts such as cutting and pollution due to agricultural, industrialization and urbanization activities. Mangrove forests are often regarded as unpleasant environments with little intrinsic value. Mangrove forests perform valued regional and site-specific functions. Mangrove species can take up excessive nutrients and also play a crucial role in creating a favourable environment for a variety of chemical, biological and physical processes that contribute to the inorganic removal and degradation of organic compounds. Of the many mangrove species Sonneratia alba, Avicennia alba and Rhizophora apiculata have been studied for biomonitoring of toxic heavy metals elements (Fe, Cu, Zn, Pb and Mn) in a wide range of plant tissues (roots and leaves) and sediment composition at three different locations in Negeri Sembilan, west coast of Malaysia. The results established that there were significant differences between the three mangrove species, locations, plant tissues and sediment samples and their interaction for all the five heavy metals content. The findings revealed that leaf tissues for all species accumulated mostly Fe, Zn, Pb and Cu. Interestingly we noticed that different localities will accumulate different type of heavy metals, for instance R. apiculata leaf tissues were detected with higher concentration of Cu and Pb at Kampung Sungai Sekawang whereas in another two sites were detected with Cu, Fe, Zn and Pb. S. alba indicated that the most heavy metals highly accumulated was Zn followed by Pb and Cu. In this study A. alba showed Zn was highly accumulated in leaf tissues at Pasir Panjang. Thus, those three mangrove species appear to have the greatest potential for use as an effective ecological indicator tools for inorganic contaminants monitoring in mangrove ecosystems
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".