The Systems Measurement of Mammalian Biotas, Part Two
Bibliographic record
Abstract
For a recent publication, the authors identified a seven-region model of mammal family distribution patterns, in which each unit contributes equally to the system's overall statistical characteristics of diversity, despite its individual units having measurably different levels of diversity and endemism. This systemization presents a highly efficient descriptive model that can possibly be interpreted as a form of natural classification. An additional analysis of the same mode is described here, in which the seven-region model of the distribution of mammal families' spatial affinities is shown to closely approach a most-probable-state arrangement, as assessed through combinatorics, raising some important questions about how macroevolutionary patterns might self-organize spatially. One of the possible practical applications of the overall approach is to areal representation; statistical moments of the underlying world patterns can be used to characterize faunal statuses at any individual location by relating the latter to the former. Through this approach, classical concepts such as corridors, tracks, and transition zones might be re-examined in a manner that better lends itself to hypothesis testing. An arbitrarily chosen bounded area, the conterminous United States, is treated in this fashion by way of illustration.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".