The effect of 135,000 years of changing climate on the global landscape
Bibliographic record
Abstract
Introduction The previous chapter provided a general review of the changing climate on Earth during, and subsequent to, the last but one glacial maximum. We reviewed proxy evidence and climate model simulations, particularly focusing on a recently completed 122,000-year transient or time-series simulation prepared for this book. In this chapter we examine the impact of that changing climate on the landscapes in which early humans lived and subsequently migrated. Of particular interest are the effects on vegetation. Vegetation provides fodder that promotes game populations and various vegetable, fruit and cereal crops for humans. Its absence implies deserts that can act as barriers to habitation and migration. In this chapter we again use proxy evidence combined with UVic Earth system climate model (UVic ESCM) simulation results, particularly the 122,000-year time series, to ascertain changing vegetation over the LGC. The UVic ESCM consists of a three-dimensional (3D) ocean general circulation model coupled to a dynamic–thermodynamic sea-ice model, an ocean carbon-cycle model, a dynamic energy-moisture balance atmosphere model, a land-surface model and a terrestrial vegetation and carbon-cycle model (Ewen et al ., 2003; Matthews, Weaver, Eby et al ., 2003; Matthews, Weaver, Meissner et al ., 2003; Meissner et al ., 2003; Weaver et al ., 2001). As stated in the previous chapter, we also included changing land-ice extent and thickness in model simulations. The coupled vegetation component of the model defines the terrestrial biosphere in terms of soil carbon, five plant functional types (PFTs) and barren ground.
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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 | 0.000 | 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) | 0.009 | 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".