Simple assessment of needleleaf and broadleaf chlorophyll content using a flatbed color scanner
Bibliographic record
Abstract
Total chlorophyll a and b content (Chlab) of leaves is an important indicator of the photosynthetic capacity, nutritional condition, and health status of plants. Developing low-cost, easily accessible methods for estimating foliar Chlabof needleleaf species would enable a broad range of forestry applications. We evaluated data acquired using an off-the-shelf flatbed color scanner to assess its utility in quantifying needleleaf Chlab. Red and green digital numbers (DN) of the scan image were obtained from needle leaves of oneseed juniper ( Juniperus monosperma (Engelm.) Sarg.) and piñon pine ( Pinus edulis Engelm.) in addition to two broadleaf species for comparison purposes. Values of laboratory-determined Chlab(range 1.5–64.0 µg·cm–2) were then predicted using the DN values from the scanner-imaged needle leaves as a regression estimator. The red or green DN values of the scanner-imaged needle leaves were curvilinearly related to Chlabwith an r2of 0.67 (RMSE = 4.72 µg·cm–2, p < 0.001) for juniper needles and an r2of 0.54 (RMSE = 5.51 µg·cm–2, p < 0.001) for pine needles. Although our results suggest that flatbed scanner derived Chlabestimates are not suitable for applications where highly accurate Chlabestimates are required, the technique is likely to be a useful tool for forest practitioners in managing tree nutrition and health.
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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 | 0.000 | 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".