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 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 | 0.005 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".