Distinguishing <i>Impatiens capensis</i> from <i>Impatiens pallida</i> (Balsaminaceae) using leaf traits
Bibliographic record
Abstract
Impatiens capensis (Meerb.) (orange jewelweed) and Impatiens pallida (Nutt.) (yellow jewelweed) are annual species with similar phenotypes that grow in similar environments throughout the eastern United States. This makes them extremely difficult to distinguish when (chasmogamous) flowers are absent. We use morphometric analyses to identify leaf characters that distinguish these species. After collecting and scanning 342 leaves from plants of each species growing in co-occurring populations in Madison, Wisconson, USA, we quantified: leaf size, shape (using elliptical Fourier analysis), serratedness, and color. Using leaf size and shape traits, a linear discriminate analysis assigned up to 100% of leaves to the correct species. The uppermost fully expanded leaf yielded the most accurate species assignments based on size and shape traits. This leaf was, on average, smaller, less deeply serrated, with a more acute base, apex, and elliptical shape in I. capensis compared with I. pallida. Leaves of I. pallida had more color contrast (lighter veins and margins) than I. capensis, which were solid green throughout. Morphometric analysis is a promising technique to identify species-distinguishing characters in the absence of binary traits or molecular genetic analyses. Leaves from across these species’ ranges should be analyzed to test the robustness of the species-distinguishing characters we present.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".