Bibliographic record
Abstract
It is of basic importance to understand the hereditary mechanisms that function in the perpetuation of the existing natural races and species and in the evolution of new ones, but investigations in this field are difficult and have largely been neglected. The commonly used laboratory organisms are inadequate to resolve differences that characterize natural biological entities found in the wild" -Clausen and Clausen and Hiesey (1958) outlined three major areas in which data were needed: (1) genetic analysis of the traits involved in local adaptation, (2) understanding acclimation, or the plasticity of traits and gene expression across ecologically relevant conditions, and (3) understanding the pathways that underlie such acclimation and adaptation. Much has changed in the past 50 years since their seminal work. First and foremost, the work of Clausen, Keck, and Hiesey (1940, 1948) has inspired a tremendous amount of effort in the fields of ecological genetics and evolutionary ecology to study ecologically important traits with statistical methods that describe population-level patterns of genetic and phenotypic variance. Such quantitative genetic approaches can provide many basic answers, including predictions of the evolution of traits, 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".