Alternative interpretation and scale-based context for “No evidence of recent (1995–2013) decrease of yellow-cedar in Alaska” (Barrett and Pattison 2017)
Bibliographic record
Abstract
In their analysis of resampled and remeasured plot data from the USDA Forest Service Forest Inventory and Analysis (FIA) program, Barrett and Pattison (2017, Can. J. For. Res. 47(1): 97–105, doi: 10.1139/cjfr-2016-0335 ) suggest that there is neither evidence of a recent regional decrease in yellow-cedar (Callitropsis nootkatensis (D. Don) Oerst. ex D.P. Little) live tree basal area nor a decrease in the species’ extent in southeastern Alaska. Here, we identify substantial, broad-scale agreement between their estimated extent of concentrated yellow-cedar mortality and that resulting from a complementary, existing body of research into yellow-cedar decline spanning 35 years. However, we also discuss concerns that the FIA remeasurement data used did not match the spatial distribution of the decline (e.g., excluding areas of known active decline in wilderness areas) and that the temporal coverage of FIA data (1990s to 2000s) was inappropriately compared with a cumulative decline map that spans several decades, meshing recent mortality with mortality that occurred up to a century ago. We provide an alternative explanation of Barrett and Pattison’s results in the context of ongoing yellow-cedar distribution and decline research in southeastern Alaska and support our interpretation by focusing on the temporal and spatial aspects of decline.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".