Holocene climate history of British Columbia using pollen-based climate reconstruction techniques
Bibliographic record
Abstract
Using pollen data to reconstruct past climate requires testing to understand the strengths and weaknesses of methods, and to develop accurate interpretations. This thesis presents the development of a British Columbia (BC) modern pollen dataset (BCMPD) for Holocene paleoclimate reconstruction. The BCMPD used to test seven climate reconstruction techniques including the new Non-Metric Multidimensional Scaling / Generalized Additive Model method. The BCMPD includes assemblages from lacustrine, bog and terrestrial depositional environments. Depositional site characteristics may affect the ability of pollen-based climate models to predict climate accurately. Results show that combining samples from multiple depositional environments can increase, decrease or have no effect on model error, independent of sample size. The effects vary among model types and across climate variables. Given the variable effects of depositional environments, researchers are recommended to test the effect of mixed depositional environments prior to model calibration when using large datasets. The accuracy of pollen datasets for climate reconstruction depends on turnover rates in source vegetation. Correlations to weather station records from the lower mainland of BC show that aerial pollen, pollen from a lacustrine sediment core and the BCMPD all show different pollen-climate relationships. These differences are likely the result of interplay between individual-level physiological responses to climate and changes in regional vegetation as a response to either succession or climatic change. Pollen may be a weak predictor of rapid climate change in forests dominated by long-lived species. Climate reconstruction in BC during the past 10,000 years indicates changes in temperature and precipitation that compare well to other proxy observations. These changes can be explained by interactions between changes in solar insolation in addition to dominant weather systems such as the Aleutian Low and Pacific High. Vegetation reconstructions using pollen-based climate models show that coastal and northern boreal vegetation zones declined in area while interior-type vegetation increased in area during the last 10,000 years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".