The characterization of complex continuous norms of reaction
Bibliographic record
Abstract
Recent focus on the array of phenotypes expressed under differing environmental conditions, or phenotypic plasticity, has led to increased understanding of its genetic basis as well as its adaptive significance. However, the quantification of plasticity has proven difficult, hampered by both the limited number of environments over which plasticity may typically be assessed and by the need to assume, a priori, the general form of reaction norms under study. Our understanding of the shapes of continuous norms of reaction and, consequently, the subtle differences that may exist in shapes among genotypes or populations is rudimentary. Here, we propose the use of the loess smoothing function to analyze complex norms of reaction and to quantify total plasticity over many environments. A thermogradient incubator offers an ideal means to provide many environments for a demonstration of the use of the loess method. We test seed germination in three populations of two monocarpic plant species for population differentiation in plasticity to temperature. First, we test for differentiation in norms of reaction to 30 temperature environments among three populations of the monocarpic perennial, Lobelia inflata. The second demonstration assesses plasticity to eight temperature environments of three populations of the arctic-alpine annual, Koenigia islandica. Our demonstration shows that the loess technique can detect significant genetic differentiation among populations in complex norms of reaction for both species studied, and suggests that the use of this procedure should be considered where the form of norms of reaction might be complex. The general applicability of the approach is discussed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".