HISTORICAL CLIMATOLOGY OF THE SOUTHERN YUKON: PALEOCLIMATIC RECONSTRUCTION USING DOCUMENTARY SOURCES FROM 1842-1852
Bibliographic record
Abstract
i Acknowledgements iii List of tables vi List of figures vii Chapter 1: Introduction 1 Chapter 2: Literature Review 11 Chapter 3: Methodology 20 Part 1: Establishing Reliability 20 Part 2: Coding Scheme 28 Part 3: Quantification 41 Part 4: Identification of Normal and Extreme Conditions 44 Part 5: Comparison to Modern Record 49 Chapter 4: Results 55 Chapter 5: Discussion 72 Chapter 6: Conclusion and Recommendations 94 References: 97 Appendix 1: All sources consulted 104 Appendices 2a-c: Transcribed journal entries: 2a) Frances Lake: Example 112 2a) Frances Lake: All transcribed entries see attached CD 2b) Pelly Banks 114 2b) Pelly Banks: All transcribed entries see attached CD 2c) Fort Selkirk 115 2c) Fort Selkirk: All transcribed entries see attached CD Appendices 3 (a-c): Monthly subcode totals: 3) Example of monthly subcode totals Fort Selkirk 117 3a) Monthly subcodes – Frances Lake see attached CD 3b) Monthly subcodes – Pelly Banks see attached CD
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".