Leaf Margin Analysis: Taphonomic Constraints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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 it