Bibliographic record
Abstract
Vegetation dynamics at regional to subcontinental scales are complex, and our understanding of the critical factors which govern them is far from complete. Temperature operates on a roughly longitudinal gradient across North America, while precipitation gradients are roughly latitudinal. What results is a complex climate space which is then further subdivided by topography, underlying geology, surface and subsurface hydrology, and seasonality, to name but a few, into broad-scale vegetation zones, or ecoregions. I used a combination of approaches to investigate climate and vegetation dynamics at multiple spatiotemporal scales, and to develop new tools to study past climate. Defining and delineating past ecoregions has long presented a challenge. Fossil pollen distinguishes between major biome types, i.e., grassland versus forest, with great success. However, distinguishing between prairie types at regional to subcontinental scales using the pollen record has been impossible. The ratio of Ambrosia (ragweed) to Artemisia (sagebrush, wormwood, mugwort) pollen has been shown to differentiate between tallgrass, mixed grass, and shortgrass prairie over a small portion of the modern Great Plains of North America. I extended that technique, and showed that the log-transformed Ambrosia to Artemisia ratio can reliably distinguish between subregions within the Great Plains, as well as regions immediately adjacent. In addition, I found that the relationship between Ambrosia and Artemisia pollen is best explained by precipitation rather than temperature, and that it produces reliable precipitation estimates when used to create models. This will allow for better reconstructions of past climate and improve delineations of past ecoregion boundaries. Pollen data are routinely used in paleoenvironmental studies to understand past climate and vegetation. One existing limitation in working with pollen data is the need to write code in order to execute several of the routine analyses in paleoecological work. I developed GeoPollen, a Shiny Dashboard application, to be a streamlined, user-friendly GUI-based tool for performing these basic analyses. Users are able to utilize more than 3,000 publicly available pollen datasets from the Neotoma Paleoecology Database spanning the last 22,000 years from the United States and Canada. GeoPollen performs a suite of common tasks on demand and generates diagnostics necessary for evaluating results. I developed GeoPollen in order to increase the openness and accessibility of late Quaternary pollen data. Boundaries between vegetation types are often highly sensitive to perturbations in climate. For example, the tallgrass prairie-temperate forest ecotone in Minnesota shifted rapidly and repeatedly during the mid-Holocene climate optimum, a warm
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".