Negative Halotropism Expressed by 3-input-3-output Tomato Root Arithmetic-Logic Gate
Bibliographic record
Abstract
Root foraging is affected by environmental stimuli that dictates its propagation vector. Multiple-root interaction has the potential to provide relevant information about asynchronous systems like the natural soil system. In this study, tomato root was used as the information producing element in three fabricated three-input-three-output negative chemotropism root logic gates: synchronized (rNHG1), and desynchronized (rNHG2 and rNHG3). Specific output channels in rNHG2 and rNHG3 were fertigated with highly acidic sodium chloride solutions (30 mM and 75 mM) and only one channel was enriched with control solution providing additional desynchronization in the system computing the logic functions of $(x,y,z) = (y + z + x\bar z)$ for rNHG1, $(x,y,z) = (xy + xz,y + z + x\bar z)$ for rNHG2, and $(x,y,\bar z) = (\bar xyz + x\bar yz,y + z + x\bar z)$ for rNHG3 where x, y, and z represent the presence of root in the output channel, whereas negated variables indicate the absence of root. Overall, root channels fertigated with control nutrients for all root logic gates resulted in the same Boolean function and negative halotropism not only repelled the growth direction of root, but it also reduced the radicle, xylem, and phloem diameters. As the root channels become more desynchronized, root fresh and dry weights decreased due to energy utilization in bending response. Here, it was proven that plant roots manifest computation and signaling based on repellant chemical. Hence, the developed root logic provides valuable information that can be extended in developing plant root-based robotic applications.
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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) | 0.002 | 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".