Bibliographic record
Abstract
Abstract Leaf margin analysis (LMA), which is based on a correlation between the proportion of woody dicot species with non-toothed leaf margins and mean annual temperature, has been promoted as a tool for estimating mean annual temperature (MAT) from fossil-leaf assemblages. The original LMA calibration was based on East Asian mesic vegetation, and substantially the same relationship has been shown for other geographical regions, including Australian mesic vegetation. In this report, taphonomic effects are assessed using autochthonous samples from extant Australian forests for sites ranging from tropical lowland rainforest and monsoonal deciduous woodland to temperate rainforest with and without emergent Eucalyptus, and for parautochthonous and allochthonous (i.e., streambed) leaf accumulations. MAT was estimated within the binomial sampling error of the estimate for 27 of 30 (90%) of the test sites, and was found to underestimate MAT systematically when applied to streambed leaf assemblages. This result may reflect the streamside bias detected in recent studies of tropical forests in South America. Sites where MAT was overestimated are of low species richness (<10 spp.).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".