Advanced Software, applied with the Polygraph technique, is useful to interpret electrochemical signals, emitted from Superior Plants (Gymnosperms), during the Depatterning procedure, with the aim to alter the biophysical state, of the same vegetal, and interpret elaborations through a procedure of reverse engineering
Bibliographic record
Abstract
Background: Plant organisms, in addition to electrochemical communication, which occurs both between plants belonging to the same species and between plants belonging to different species, are also supposed to communicate through other means, defined by the discipline known as Plant Neurobiology. Acting with the method called Depatterning, defined by the Canadian psychiatrist D.E. Cameron, and used as a Reverse Engineering procedure, the aim is to cancel the "consciousness" of the vegetable, to understand if they are in possession of the same consciousness. Methods: A plant organism, Dracaena fragrans, was connected to a digital polygraph, in order to monitor its parameters and, successively, doses of a psychotropic compound such as N, N-Dimethyltryptamine (N, N-DMT), and electrical impulses supplied by a Tesla coil. By interfacing directly with the plant, through direct questions, and waiting for the computerized re-elaboration of the answer, data relating to Plant Depatterning were obtained. Results: The computerized processing of the alteration of the responses provided by the polygraph (vegetable connected to the polygraph), with respect to the control condition, has made it possible to define the significance of the effect of both the psychotropic compound and the electrical discharges, in the alteration of the same responses. Conclusions: Based on the interpretation of the data obtained, it is possible to affirm the positive effect of the Depatterning procedure, in the alteration of the biophysical state of the plant.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".