Large apparent growth increases in boreal forests inferred from tree-rings are an artefact of sampling biases
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Tree rings are thought to be a powerful tool to reconstruct historical growth changes and have been widely used to assess tree responses to global warming. Demographic inferences suggest, however, that typical sampling procedures induce spurious trends in growth reconstructions. Here we use the world’s largest single tree-ring dataset (283,536 trees from 136,621 sites) from Quebec, Canada, to assess to what extent growth reconstructions based on these - and thus any similar - data might be affected by this problem. Indeed, straightforward growth rate reconstructions based on these data suggest a six-fold increase in radial growth of black spruce ( Picea mariana ) from ~0.5 mm yr −1 in 1800 to ~2.5 mm yr −1 in 1990. While the strong correlation (R 2 = 0.98) between this increase and that of atmospheric CO 2 could suggest a causal relationship, we here unambiguously demonstrate that this growth trend is an artefact of sampling biases caused by the absence of old, fast-growing trees (cf. “ slow-grower survivorship bias ”) and of young, slow-growing trees (cf. “ big-tree selection bias ”) in the dataset. At the moment, we cannot envision how to remedy the issue of incomplete representation of cohorts in existing large-scale tree-ring datasets. Thus, innovation will be needed before such datasets can be used for growth rate reconstructions.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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 it