Analysis of Cadmium (Cd) Heavy Metal on Sediment and Mangrove Leaves <i>Avicennia marina</i> at Mangrove Ecotourism Wonorejo, Surabaya
Bibliographic record
Abstract
Mangrove plants have the ability to accumulate heavy metals and help reduce the level of concentration of contaminants in the water. Sediments and Avicennia marina leaves potentially become indicators of heavy metal content of cadmium in an area. The content of heavy metal cadmium in Mangrove Ecotourism Wonorejo, Surabaya is not known for certain. Based on the environmental quality standard guidelines according to Decree of Minister of Environment Number: KEP-MEN LH No.51/MENKLH/2004, cadmium threshold for biota is 0.001 ppm. Cadmium threshold in sediment refers to the Canadian Council of Ministers for the Environment (CCME) at 0.7 ppm. The purpose of this research is to learn the cadmium heavy metal content on soil sediments and mangrove leaves of Avicennia marina in Jagir and Aftur rivers in Mangrove Ecotourism Wonorejo Area. The research method used was descriptive method with field survey and sampling then testing by Atomic Absorption Spektrophotometry (AAS). The supporting parameters observed in this study are pH, temperature, and salinity. The results of Atomic Absorption Spectrophotometry (AAS) show that in Jagir River Mangrove Ecotourism Surabaya Area, the highest content of cadmium (Cd) heavy metal in sediment and Avicennia marina leaves is 1.025 ppm and 0.602 ppm respectively. The content of cadmium (Cd) heavy metal is highest in the sediments and Avicennia marina leaves in the Aftur River Mangrove Ecotourism Wonorejo Surabaya area at 1.003 ppm and 0.622 ppm respectively. Coefficient of sediment correlation and Avicennia marina leaves in Jagir river equals to 0.932 and in Aftur river is at 0.929. The shape of the sediment correlation and Avicennia marina leaves is positive with a very strong correlation relationship.
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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 | 0.000 |
| 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".