Norms of reaction and adaptational value considered in a tree breeding context
Bibliographic record
Abstract
Separability of genetic from environmental effects on target traits is a central concern of each breeding program. This requires studies of norms of reaction, i.e., responses of genotypes to environmental conditions that potentially participate in the modification of the trait. Yet, in addition to this modifying environmental condition, achievement of the breeding goal can be affected by a second component of the environment, which decides upon the adaptedness of the desired trait expressions. These adaptive environmental conditions may also vary and may be associated in various ways with the modifying conditions with the result that the desired phenotype is adaptively inferior to less desired phenotypes. Therefore, it is important to know the prerequisites under which a consistent phenotypic superiority guaranteed by separability of the genetic effects transforms into consistent adaptational superiority relations among genotypes. After having recalled the system analytic basis of adaptational processes, this problem is tackled with the help of a paradigm model in which photoperiod dynamics modifies growth conclusion in forest trees and first frost decides upon the adaptedness of the time of growth conclusion in terms of the realized annual growth increment. In this paradigm, maximization of growth increment constitutes both the breeding goal and adaptational valuation of the trait "growth conclusion" under the adaptational condition "first frost." The central result of the analysis rests on the definition of a fictitious genotype, whose time of growth conclusion equals the time of first frost, and which thus characterizes the association between modifying and adaptive conditions that guarantees maximum adaptedness. The result then states that separability of the genetic effects on growth conclusion within the set of genotypes enlarged by the fictitious genotype implies consistent adaptive ranking among genotypes. The implications for common breeding practice and its evolutionary consequences are 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".