Bibliographic record
Abstract
The European Bird Cherry plant (scientifically known as Prunus Padus L.) is a non-native deciduous tree that is quickly displacing native trees in many regions of northern US and the State of Alaska. The need for its eradication is urgent and critical. In this paper, we introduce a copula-based method for Bird Cherry plant identification. This method uses leaf rich and contextual features and copulas ensemble to proficiently identify the plant. To evaluate its performance, we experimented on a large dataset of 2,000 leaf images of bird cherry leaves, sour cherry leaves and red oak leaves. We compared our method performance results with published deep learning methods and found that our results are comparable or better. Finally, this method is fast, and has low computational complexity which makes it an ideal solution for binary plant identification especially for the sought-after medicinal plants. This method can be converted to an application for consumer technology devices such as smartphones and hand-held devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".