Bibliographic record
Abstract
A growing number of empirical and theoretical studies suggest that naturally occurring intraorganismal genetic heterogeneity (IGH) may be more common than previously believed (for review see Pineda‐Krch & Lehtilä, 2004). That the occurrence of genetically heterogeneous organisms in nature is of deep biological significance was recognized by Williams (1966) in his seminal Adaptation and Natural Selection. Nevertheless, the matter has received little attention in ecological and evolutionary research, and issues such as the frequency, the mechanisms and the potential significance of IGH remain largely unexplored. There seem to be two reasons for this omission and the general lines of argument can be summarized as follows: (i) it is important to maintain genetic homogeneity of an organism in order to avoid intraorganismal conflicts, hence IGH will be rare because there is strong selection against it and (ii) genetically heterogeneous organisms do not qualify as true gene vehicles and do not adhere to the predominantly accepted definition of individuality. Although the potential for IGH is acknowledged its significance is dismissed either because of its assumed rarity or the perceived difficulties in accommodating the idea within contemporary biological concepts. This line of reasoning is at least partially justified. That IGHs will often entail costs at the organism level is probably true, but currently we know little about the true distribution of effect, and as several of the commentaries show there is more to the story than what the statement suggests (Rinkevich, 2004; Santelices, 2004; Strassmann & Queller, 2004). It is also true that it would be difficult to accommodate IGH into the contemporary concept of individuality as, ever since Weismann, individuality has implied genetic homogeneity (Santelices, 1999). While several of the commentaries discuss the need for a revised individuality concept to accommodate situations such as IGH, Hutchings & Booth (2004) also appropriately point out that it is unlikely that any given definition will ever be applicable to all types of organisms. Indeed, the complexity of defining the individuality concept has been addressed on several occasions (Tuomi & Vuorisalo, 1989; Santelices, 1999). Nevertheless, a widely accepted conceptual revision has not yet occurred. Incorporating IGH into a revised concept of individuality will pose interesting challenges to theoretical and empirical research alike.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".