THE IDENTIFICATION AND INTERPRETATION OF MICROBIAL BIOGEOMAGNETISM
Bibliographic record
Abstract
The efforts of many people, both inside and outside of Caltech, have allowed me to complete this dissertation. My advisor, Joe Kirschvink, played a particularly critical role. Over the last five years, he has been extraordinarily generous with his time, thoughts, and resources. Nearly as soon as I arrived in Pasadena in July 2002, I left to spend a month with Joe traveling through the Paleoproterozoic in South Africa and Canada. He has since taken me to explore the early Precambrian in Western Australia and the late Precambrian in South Australia and sent me to China to investigate the record of the Permo-Triassic extinction there. Such global travels have not only helped me understand many of the great geological questions of geobiology; by exposing me to different nations and different economic situations, they have played a major role in shaping my motivations for my future work. Joe has also been exemplary in the classroom in exciting students about the Earth sciences and has provided me with abundant opportunities to develop my own teaching skills. My favorite student evaluation from an Earth history class I helped Joe teach in 2006 reads, “Kopp and Kirschvink are a fascinating pair. The class is almost worth taking just to hear them banter. ” Joe is trusting, open, and in many ways selfless with respect to his students,
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".