Reply to formal comment on Griffiths et al. (2017) submitted by Gajewski (2020)
Bibliographic record
Abstract
Gajewski offers a formal comment on Griffiths et al. (2017), a paper that explored how microclimates and their varying ice cover regimes on lakes and ponds in Arctic regions modified the diatom assemblage responses to recent warming. One of Gajewski's main criticisms is that the microclimate classification scheme used in Griffiths et al. (2017) is merely anecdotal; a claim which ignores the value of observational evidence and misunderstands the frequency that each site was visited or surveyed. We clarify that the study sites were visited multiple times via recurrent aerial surveys and ground observations dating back to the 1970s, which supports our microclimate classification scheme. Many of Gajewski's claims regarding climate, catchment characteristics, and ice melting properties from field locations he has not visited were refuted by veteran Arctic scientists with long-term field experience in these regions. In addition, Gajewski makes several criticisms concerning radioisotopic dating, core chronology, sediment mixing, diagenesis, and preservation of bioindicators that relate more to general paleolimnological assumptions than to conclusions reached by Griffiths et al. (2017). Research from the 1980s and 1990s, when scientific consensus on these issues was first reached, readily show that the methodologies and data interpretation of Griffiths et al. (2017) are sound. We appreciate the opportunity to expound on the finer details of the Griffiths et al. (2017) paper, work based on field research by the study's co-authors spanning almost three decades, with additional observations from colleagues dating back to the 1970s. We address Gajewski's criticisms with relevant literature, expert statements, and a few clarifying comments.
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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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.071 | 0.080 |
| Insufficient payload (model declined to judge) | 0.011 | 0.013 |
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".