Bibliographic record
Abstract
I propose that sexual dynamics, through mechanisms of sexual selection, can in part determine what constitutes specific breeding habitat. In this view, breeding-habitat features chosen by organisms, like certain morphological or other behavioral traits they exhibit, can be sexually selected, with the consequence that breeding habitats may not be uniquely aligned for ecological niche requirements. I distinguish sexual selection from natural selection because I mean to contrast sexual natural selection (sexual selection) from nonsexual natural selection (ecological selection). Thus, I suggest that ecological selection, acting on traits related to physical resources, and sexual selection, acting on traits related to mate choice, are potentially conflicting forces acting on breeding-habitat specificity. It is hardly novel to contend that sexual relations influence spatial patterns, but those influences have always been believed to operate within the confines of habitat sculpted by ecological selection. Here, sexual selection defines, in part, what constitutes breeding habitat. Certain predictions arise if sexual selection generates breeding-habitat specificity. Breeding habitat must be specific, though regional differences, including dramatic ones, are consistent with the idea. A shift in, or relaxation of, such specificity in nonbreeding situations is expected, given the flexibility in exploiting resources. Generalized traits, such as beaks equipped to exploit a variety of food sources, are predicted to prevail, because adaptive constraints from one period may compromise adaptive solutions for another. Finally, we would expect social factors to influence habitat occupancy
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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".