Characterization of novel Phophatase from the genome of Genlisea aurea An in silico approach
Bibliographic record
Abstract
Insectivorous plants use enzymes to digest their prey. These plants found in the tropical areas like forest of east India. Mostly insectivorous plants produce their own digestive enzymes to digest their captured insects and small animals diverge from protozoa to invertebrates. The plants need extreme sunlight and rainwater to sustain. These plants consume insects to suck the nutrients from the pray since the plant grows in nutrient less soil especially in nitrogen and potassium. The studies have shown the digestive enzyme from the plants has the proficiency to fight against the various diseases in human like Cancer, Diarrhea, Cholera, Hepatitis, Digestive process related diseases also the phytochemicals found in the insectivorous plants shows resistance against the various metabolic targets of numerous human diseases. Our study has collected 810 putative digestive enzymes with blast hit and domain search; we have characterized the full enzymes using partial sequence as a templet and predicted the function. The structure modelling has done for the phosphatase enzymes using I-Tesser server. Our future study includes in vitro identification of digestive enzymes in Genlisea aurea and its further application in degrading the waste materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".