Provenance variation in carbon isotope discrimination of mature ponderosa pine trees at two locations in the Great Plains
Bibliographic record
Abstract
We analyzed genotypic variation in carbon isotope discrimination (Δ), photosynthetic gas exchange, and needle morphology of ponderosa pine (Pinus ponderosa Dougl. ex Laws.) trees from four seed sources growing in two 26-year-old provenance plantings near Plattsmouth, Neb., and Norman, Okla. The populations studied were from South Dakota, New Mexico, Wyoming, and Nebraska. Net photosynthesis (A) and needle conductance to water vapor (gwv), were measured during the growing season of 1994. Specific leaf area, stomatal density, and Δ were analyzed in needles grown from 1991 to 1994. The southernmost source (New Mexico) had the highest intrinsic water-use efficiency (A/gwv) among the sources studied. Carbon isotope discrimination was correlated with growth (r = -0.81, P < 0.05), A/gwv (r < -0.54, P < 0.001), and gwv (r > 0.46, P < 0.05) but not A. Variation in Δ was significant among seed sources and years (P < 0.001). We observed a strong genotype × environment interaction in Δ resulting from geographic location but not moisture availability within locations. We hypothesize that the genotype × environment interaction is related to variation in growth phenology among the seed sources. Improving water-use efficiency or growth of ponderosa pine via Δ will require an understanding in genotypic variation in growth rhythms.
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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 | 0.000 |
| 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 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".