Carbon isotope discrimination of tall fescue cultivars across an irrigation gradient
Bibliographic record
Abstract
Carbon isotope discrimination (Δ) has been proposed as an indirect criterion for evaluating water-use efficiency; however, limited information is available regarding the relationship between and dry matter yield (DMY) at different levels of irrigation. Ten cultivars of tall fescue (Festuca arundinacea Schreb.) were established in the field under a line-source irrigation system and defoliated five to six times per growing season. Objectives of our 2-yr study were to evaluate: (i) trends in Δ across a gradient of four water levels (WL-2, wettest to WL-5, driest) at a mid-summer harvest, (ii) the consistency among grass cultivars at one WL across two harvest dates, and (iii) relationships between Δ and DMY within and across water levels. Consistent and significant differences (P < 0.05) were found among cultivars for Δ when evaluated at WL-2 for Harvests 2 and 4 in successive years. Cultivars also differed significantly for Δ (P < 0.01) across four water levels when evaluated at Harvest 4. Relative differences among cultivars were consistent across water levels and between harvest dates. Although a curvilinear trend was evident for some cultivars, mean Δ values decreased in a near linear manner from WL-2 to WL-5. Based on orthogonal polynomials, the mean trend across water levels was partitioned as 95% linear and 5% quadratic. Correlations between Δ and DMY were nonsignificant at the higher water levels (WL-2 and 3), but correlations were positive at the lower water levels (WL-4 and 5). We conclude that under lower water levels, selection for low Δ in tall fescue cultivars will likely lead to decreased forage yield. Key words: Festuca arundinacea, forage yield, water-use efficiency, line source, delta
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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 | 0.000 |
| 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 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".