Effect of Heavy Metal Pollution on Leaf Litter Decomposition of Two Species of Mangroves, Avicennia marina and Rhizophora mucronata
Bibliographic record
Abstract
Decomposition of litter is influenced by physicochemical characteristics of the habitat which is affected by pollution. In this study the effect of heavy metals on leaf litter decomposition of two mangrove species, Avicennia marina and Rhizophora mucronata is investigated. An experiment was conducted in which litter bags were half-buried in mangrove soil in earthen pots in a greenhouse in which close to natural conditions were maintained and they were treated with 0, 5 and 10 ppm Ni and Pb as a solution of sea water for 16 weeks. Periodic observations were taken on the dry weight remaining and the four factor ANOVA was performed. All four factors (species, heavy metals, concentrations, time) were found to be significant (P at the most 0.05) while some of the interactions were also significant. Half-life and rate of decomposition, k were calculated on the basis of 12 periodic weight loss observations. The litter decomposition followed an exponential decay curve in all cases. The highest rate of decomposition (0.0155 gDWd-1) and the shortest half life (7.44 days) were found for the control of Avicennia marina. In general, decomposition rate of A. marina was more rapid compared to that of R. mucronata. Whereas, the decomposition in treatments with heavy metals were found to be slower than in the controls, the minimum of which (0.0105 gDWd-1) and the longest half life (18.17 days) were found in Rhizophora mucronata leaves treated with 10 ppm Pb. Lead appears to be more inhibitory to the process of litter decomposition compared to nickel. The two mangrove species responded differentially to the heavy metal concentrations. The influence of heavy metals in the decomposition process is discussed.
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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) | 0.000 | 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".