Genetic and Demographic Criteria for Defining Population Units for Conservation: The Value of Clear Messages
Bibliographic record
Abstract
Abstract In a recent paper on Harlequin Duck (Histrionicus histrionicus) interannual site fidelity (Iverson et al. 2004), we concluded that wintering populations were demographically structured at a finer geographic scale than that at which genetic differentiation was observed and that conservation efforts should recognize this degree of demographic independence. In a critique of our study, Pearce and Talbot (2006) contend that our measures of fidelity were not robust and imply that in the face of “mixed messages” we failed to appreciate the role of genetic data in defining population units. We recognize, as we did in our original paper, that our methods for quantifying site fidelity have some limitations; however, the patterns in our data are consistent with a considerable body of literature indicating high winter site fidelity in Harlequin Ducks. Moreover, we do not consider differences in the scales at which genetic and demographic structure are expressed to be “mixed messages,” given the different spatial and temporal scales at which genetic and contemporary demographic processes operate. We emphasize that a lack of genetic differentiation does not necessarily preclude the existence of contemporary demographic structure with relevance for conservation.
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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".