Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: a comparison of methods
Bibliographic record
Abstract
Analysis of reflectance can be used to estimate foliar concentrations of photosynthetic pigments, thus providing information on the physiological status of green plants. We compared several methods of reflectance analysis for the capacity to differentiate among effects of fertilization treatments across different irradiances on seedlings of Engelmann spruce (Picea engelmanii Parry ex Engelm.). Seedlings were grown in two light treatments (0 and 60% shade) and three nitrogen (N) treatments (10, 25 and 100 mg N l-1) for one growing season, after which foliar reflectance of the needles was measured. Five indices were tested: R550 (% reflectance at 550 nm); red edge position; the ratio R698:R760; the structure independent pigment index (SIPI); and the photochemical reflectance index (PRI). Both the light and nutrient treatments significantly affected foliar chlorophyll a and b and carotenoid concentrations. Among the indices tested, R550, red edge position and R698:R760 ratio were related to chlorophyll concentration, and were significantly affected by both light and N treatments. Both SIPI and PRI were related to chlorophyll and carotenoid concentrations. Among these relationships, PRI was affected by both treatments, whereas SIPI was sensitive to N treatment but not to light treatment. All five indices were weakly but significantly correlated with growth as measured by dry weight.
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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 | 0.001 | 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) | 0.001 | 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".