Bibliographic record
Abstract
I did principal components analyses on these, and then correlated the Environmental PC scores with the Phenotypic PC scores.Lastly, to determined the association between the Environmental PC scores and the Phenotypic PC scores I used the average morphological data for males from the 42 samples for which I had good climatic data and the climatic data set (not including latitude, longitude, elevation, or the measures of species diversity).I used a redundancy analysis (SAS PROC CANCOR; SAS Institute 1985), which links the morphological data with the climatic data with a canonical correlation analysis.This can be done both ways (morphological data vs. climatic data, or climatic data vs. morphological data) with parallel principal components analyses between the two data sets where the correlation between them is maximized.Lastly, for a multivariate measure of the concordance between these two matrices, I used Procrustes Analysis (PROTEST; Jackson 1995; D. A. Jackson, pers.comm.);many more commonly used procedures for such comparisons are unsuitable because of non-linearity among locality and environmental data.I looked at the residuals from a Procrustian analysis of the two largest axes combined from both the morphological and climatic data principal components to identify from which localities the morphology was least well explained by the climatic variation.RESULTS ?The patterns of variation in the saltmarsh Savannah Sparrows from the coast of southern California and Baja California, and Sonora and Sinaloa are substantially different, and are discussed separately.I included the sample resident in the saltmarshes near Morro Bay, California, in both groups because they are phenetically intermediate (Fig. 2; see discussion below).NON-SALTMARSH SAVANNAH SPARROWS Univariate analyses of size The ANOVA' s (which are not presented here) showed significant geographic variation with regard to all 24 skeletal variables for both sexes.Appendices 1 and 2 list means, ranges, and standard deviations for the larger samples of males and females, respectively.The patterns of variation for the two sexes are similar, and a number of overlapping statistically homogeneous (snk) subsets were identified.I will only describe general trends.Birds from Sable Island, Nova Scotia, and Umnak Island in the Aleutians are the largest (Figs. 1 and 4; Appendices 1 and 2).There is clinal variation along the Alaska Peninsula, with large birds, nearly as large on average as those on Umnak Island, at the tip (Cold Bay), intermediate birds at Port Heiden, about half-way eastward down the Peninsula, and small birds at Wasilla, Alaska (near Anchorage).Birds from Middleton Island, Alaska, in the north Pacific, are also large, nearly comparable in size to birds from Cold Bay.Savannah Sparrows from the coast of maritime Canada, including those from the Magdalen Islands, Quebec, in the Gulf of St. Lawrence, are larger than those from farther inland."Ipswich" sparrows (P.s. princeps) from Sable Island, Nova Scotia, are especially large and are comparable in size (although slightly larger) with birds from the Aleutian Islands.At the other end of the spectrum, the smallest birds are from the interior of California (Owens Lake), Washington (Creston, Hoquiam), and from Nevada (Elko, Alamo), Utah (Elberta), Alberta (Milk River, Grande Prairie), Wyoming (Sheridan), the interior of Alaska (Koyuk, Wasilla, Fairbanks), and the Mackenzie River Valley, Northwest Territories (Norman Wells, Inuvik).It needs to be emphasized, however, that, with the exception of birds from Sable Island, Umnak
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.296 | 0.172 |
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".