Tree rings and volcanoes : the climate of NW North America
Bibliographic record
Abstract
This thesis aimed to refine temperature reconstruction strategies for Northwest North America (NWNA), introducing new applications of the latewood blue intensity parameter (LWBI), and pooling multiple regional datasets to create a new millennial-length temperature reconstruction. Currently, NWNA is underrepresented by long tree records in comparison to Eurasia. Such records are crucial for contextualizing modern warming in the context of past variability. The NWNA region represents the origin of the “Divergence Problem”, a phenomenon referring to the loss/weakening of temperature sensitivity over time. To address this, a strategy was developed, using a network of sites in the southern Yukon, to minimise its impact and optimise the temperature signal. Trees growing within 100m of upper treeline provide the most temporally stable signal, making them the optimal targets for a temperature reconstruction. The successful use of LWBI for developing temperature reconstructions is well-documented, yet its application for dating is less understood. LWBI was utilised to absolutely date the 9th century Mt. Churchill eruption for the first time using dendrochronological techniques, a result not possible using RW. By demonstrating that LWBI can be effectively used for crossdating between species, dated subfossil trees preserved in the volcanic ash placed the eruption between 852-853 CE. The final LWBI reconstruction provided temperature variability for NWNA back to the 3rd century CE. While full period modelling (1901-2014) explained 46% of the temperature variance, the fidelity of the reconstruction reduced significantly prior to 933 CE due to decreased replication and significant underestimation of extreme warm season years was noted. The NWNA BI-based reconstruction demonstrates the most time stable temperature signal compared to other NWNA reconstructions but struggles to capture long term trends. Future research should integrate these data for a multi-parameter reconstruction for a more comprehensive, robust reflection of NWNA temperature variability.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".