Rapid Differentiation of Sex in Calamus johndransfieldii Seedlings Using Near Infrared Spectroscopy
Bibliographic record
Abstract
Resiniferae rattans, or Dragon's blood rattans (known locally in Indonesia as "Jernang"), are highly valuable Non-Timber Forest Products from Sumatra's deep rainforests.One of these species is Calamus johndransfieldii.Dragon's blood rattans are dioecious, indicating that each individual plant is either male or female.Farmers prefer female plants as they produce economically valuable fruits.Traditionally, determining a plant's sex has been a lengthy process, requiring approximately 3-4 years from planting to fruit maturation.To address this, a novel and efficient method for sex determination using Near-Infrared Spectroscopy (NIRS) was investigated.Leaves collected from known male and female plants were analyzed using a Thermo Nicolet Antaris TM II MDS in conjunction with a portable sensing device (PSD NIRS i16) within the spectral range of 1,000-2,500 nm.Principal Component Analysis (PCA) was then applied to classify the spectral data, clearly distinguishing male from female plants.Further analysis utilized machine learning techniques, including Bootstrap Forest, Neural Booster, Support Vector Machines, and K-Nearest Neighbors, to refine predictive accuracy.The findings revealed marked differences in the spectral components of male and female plants, allowing for fast and reliable sex determination.The most effective method was identified as Bootstrap Forest, with an exceptionally low misclassification rate of 0.0811%.This rapid approach significantly reduces the waiting period for farmers, enhancing productivity and economic returns.
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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 | 0.001 |
| 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".