Palaeomacroecology: large scale patterns in species diversity through the fossil record
Bibliographic record
Abstract
Palaeomacroecology is the study of large scale patterns of species diversity in the fossil record, encompassing a variety of subtopics. This thesis also addresses a variety of these subtopics, making it difficult to define under one heading.The first portion of the thesis deals with a new package of software tools for the analysis of large scale datasets, with a specific focus towards palaeoecology and palaeogeography. These software tools have been combined into a package called fossil that has been released on the Comprehensive R Archive Network (CRAN), and is already being used by other palaeoecologists. While the majority of these tools had a basis in previous statistical methods, I have also independently developed a clustering algorithm for use with biogeographic datasets. This clustering algorithm is relational, non-Euclidean and non-hierarchical and as such is called Non-Euclidean Relational Clustering (NERC). NERC eliminates several of the assumptions common to most other clustering methods that are often violated by biogeographic data.The next portion of my thesis describes a new Triassic aged flora from Axel Heiberg Island in Nunavut. Macroecological studies typically use large databases compiled from individual samples; therefore, these individual samples represent the foundation on which macroecological analyses rest, and collection and description of new fossil bearing sites is vital to the advancement of palaeomacroecology.Chapter 5 is an analysis of the provinciality and beta diversity of dinosaurs in the Late Cretaceous of North America. This analysis found that contrary to previous studies, dinosaur genera were widespread across the continent and not restricted to small geographic ranges. Chapter 6 is the final culmination of my thesis, and where I see palaeomacroecology headed in the future. It is an analysis of how latitudinal diversity gradients in plants have changed through time. The analysis assesses the impact of changing climate in creating and sustaining the latitudinal diversity gradient, and lends support to the idea that temperatures are important drivers of the gradient.The final chapter is a summary of where palaeomacroecology has been, and where its future work might be best focused. While the field of palaeontology is vital to our understanding of large scale, especially temporally, patterns of species diversity, the field of palaeontology has an opportunity to advance our understanding at an even more rapid pace provided we ask the appropriate questions of our data.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Palaeoecology thesis on diversity patterns in the fossil record; includes an R package and a clustering algorithm, but these are domain analysis tools and the object is species diversity.
This thesis develops tools and analyzes fossil biodiversity while studying paleontology, not research itself.
Paleontology thesis on large-scale fossil diversity patterns; domain science, not metaresearch.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".