Compilation of Pleistocene Glacial Maps of North America for Botanical Explorations to Explore Alpine Plant Population Dynamics due to Glaciation Cycles
Bibliographic record
Abstract
This presentation is a summary of official geological mapped alpine and continental glaciers present during the Pleistocene epoch. These maps were created with the freely available ESRI system using government and academic maps to create shape files, as well as some existing government shape files. The maps showcase the alpine glaciers in the western United States and Alaska, as well as continental glaciers covering Canada and reaching into the northern part of the United States. The lab in which I am a part of focuses on the study of population genetics of alpine plants and the effect of climatic fluctuations during the ice ages on plant populations. Glaciation cycles of the most recent Ice Ages have resulted in species distributions known as disjunct species as their lineages were split. The history of their lineages can be studied by sequencing their DNA. As part of this multi-year project, the first step is to identify accessible localities for plant collections of specific alpine species. These locations must meet specific criteria, logistically, biologically, and geographically. We must collect a wide variety of plant lineages during the short summer field season, while still accurately representing the diversity of our chosen species. Therefore, we find field sites that are accessible relative to other field sites, that are situated within the alpine tundra, and host the specific disjunct species that we are targeting. Most importantly, the chosen localities have to have been under the influence of glaciers during the Pleistocene epoch and have nearby regions that remained unglaciated during the same period. It is in those unglaciated sites where plant lineages found refuge during the glacial maximums, and this is hypothesized to be detectable in their DNA sequence variation. After field collections of plant tissues, we will be processing them in the lab for DNA extraction and sequencing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".