The diversity of diversity studies: retrospectives and future directions
Bibliographic record
Abstract
051015202530354045505253556065707580859095100105Early View (EV): 1-EVtypical notions associated with community assembly theory. The final topic, ‘emerging methodologies’ and novel discov-eries associated with diversity studies, has been effectively reviewed at some points in time (Purvis and Hector 2000, Iknayan et al. 2014), but it was also examined in several of the included papers now. The emerging methodologies topic includes theoretical implications of these findings for ecology and evolution and a critical evaluation of previous approaches. Finally, the implications of these clusters of findings for conservation emerges from a collective reading of the special issue provide invaluable insights into some of the ‘global challenges’ (Carroll et al. 2014) that this field both faces and can help solve.Diversity is a concept that relates to most dominant subfields in ecology and evolution including distur-bance, ecosystem function, coexistence, interactions, co- evolution, population genetics, and spatial stability to name a few. Just as there are multiple dimensions to climate change effects on biodiversity (Garcia etal. 2014), we propose that there are multiple dimensions to biodi-versity that influence respective domains of inquiry in ecology and evolution. The topics proposed were selected to reflect the broadest possible scope of implications to ecologists and evolutionary biologists and in general and to ensure that the examinations of theory nonetheless address application. Future developments were the pri-mary goal of this symposium as we see this an opportu-nity to refine the semantics and appreciation of respective diversity in scales of study, approaches, and methodolo-gies. The primary objective of this special issue is to thus highlight both the empirical and theoretical opportunities and identify research gaps for the next 100 yr. Ancillary goals that were addressed to meet this overarching objec-tive include the following. 1) To critically examine scale as it relates to understanding ecological and evolutionary processes that shape patterns of diversity. 2) To develop a clear set of directions for future studies of diversity that incorporate genetics/relatedness and spatial landscapes. 3) To describe pivotal concepts and relationships that limit
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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 | 0.100 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".