Paleoecology of the Bolivian Pantanal: A 45,000 year history of vegetation and climate change in tropical South America
Bibliographic record
Abstract
This thesis is formatted in journal style, and as is typical of research submitted to peer-reviewed journals, some of the data included in the three research chapters have been collected by collaborating authors. These authors are listed at the beginning of each chapter, and the contribution of each is detailed below. Chapter 2 incorporates a vegetation survey around the shores of the field site, Laguna La Gaiba, conducted by Ezequiel Chavez and René Guillén of the Muséo de Historía Natural Noel Kempff Mercado, Santa Cruz, Bolivia, and Michael J. Burn differentiated the pollen of Moraceae and Urticaceae in twenty-five horizons. Toby Pennington of the Royal Botanic Gardens, Edinburgh, has been crucial to data interpretation and discussion. The research in Chapter 3 also relies on the field surveys of Ezequiel Chavez and René Guillén, as well as the finer-scale pollen differentiation provided by Michael J. Burn. This chapter also includes carbon isotope data obtained by Neil J. Loader and Alayne Street-Perrott, Swansea University, Wales, and elemental data collected by Francis E. Mayle and Michael H. Marshall (Aberystwyth) at the Itrax XRF scanner facility, University of Aberystwyth. As in the preceding chapter, Toby Pennington has played an integral role in the ecological interpretation and climatic significance of changes in the inundation-tolerant forest. The data obtained for Chapter 4 were collected entirely by my own hand, but the interpretation of these data was improved by contributed work of my co-authors in the previous two chapters.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".