Reconstruction of Tertiary Metasequoia forests. I. Test of a method for biomass determination based on stem dimensions
Bibliographic record
Abstract
Abstract Accurate reconstruction of the biomass, structure, and productivity of ancient forests from their fossilized remnants remains an interesting challenge in paleoecology. In well-preserved Tertiary fossil Metasequoia forests of Canada's Arctic, in situ stumps and fragments of stems, treetops, and branches contain substantial information about tree dimensions that can be used to determine tree height, stand biomass, and other characteristics such as canopy depth and structure, and the history of stand development. To validate a method for reconstructing the biomass of the Eocene floodplain Metasequoia forests of Axel Heiberg Island, we measured stump diameters and spacing, and stem, branch, and treetop characteristics in living Metasequoia glyptostroboides and Chamaecyparis thyoides stands in ways that simulate the limited measurements that can be made in well-preserved fossil forests in Canada and probably elsewhere. We used those limited measurements to estimate tree height and volume, branch and foliar dry weights, and tree biomass. The estimates derived from the limited data set are usually within 15% of the estimates derived from the methods currently used in forest ecology for determining those metrics in modern forests. Under appropriate conditions, the biomass of ancient forests can be estimated with reasonable confidence.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".