Reply to comment by Gracz on “Wetland drying and succession across the Kenai Peninsula Lowlands, south-central Alaska”Appears in the Can. J. For. Res. <b>35</b>: 1931–1941.
Bibliographic record
Abstract
Gracz (2011, Can. J. For. Res. 41: 425–428) proposes that the Good Friday earthquake of 1964 caused falling lake levels and drying wetlands on Alaska’s Northern Kenai Lowlands (NKL). His hypothesis states that the earthquake increased hydraulic conductivity by fracturing a leaky confining layer, accelerating drainage of surface water into regional aquifers. We counter that a single model of draining does not apply across the heterogeneity of geomorphology and soils on the NKL. In particular, the NKL’s glacial history precludes uniform application of a subsurface hydrologic model for lake draining and the nature of peat-based wetlands precludes its application to wetland drying. Instead, small, yet cumulative, climatic reductions in moisture surplus explain both observed lake level declines and vegetation changes. Moreover, and unlike a climatic hypothesis, a seismic hypothesis fails to explain lake drying elsewhere in Alaska. Although it is likely that the earthquake influenced some hydrologic features in the NKL, it is unlikely that a single hydrologic model based on a simple mechanical cause, e.g., downward drainage, adequately explains the changes observed across the whole NKL. Conversely, we maintain that the uniformity of the vegetation response seen across different landscapes, including wetlands, forests, and alpine areas, throughout the state of Alaska strongly supports a climatic hypothesis.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.025 | 0.031 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".