Bibliographic record
Abstract
Griffiths et al. (2017) analyzed several ponds and lakes from the Cape Herschel region of Ellesmere Island in order to "…explicitly examine the role of ice cover as the dominant driver of diatom assemblage change…". I reanalyze their data and suggest that their classification scheme, that they propose is due to differences in ice cover seasonality ("warm", "cool", "cold", and "oasis"), is confounded with other morphological and chemical variables that better explain the differences between the groups. The "cold" sites are the deepest (lakes) and differ from the small, shallow ponds that occasionally dry, which would therefore have different diatom assemblages and histories. The "oasis" sites are nutrient enriched and probably have more stable water supplies, thereby enabling an aquatic flora providing habitats for diatoms. A key part of their interpretation is that "warm" sites have responded more rapidly to recent climate change than "cold" or "cool" sites, but their chronologies do not allow for such a conclusion. There is no clear difference between "cool" and "warm" sites, and problems in dating the sequences means inferences about their histories are not supported by data. Their results, which are restricted to the past century, are contradicted by a Holocene sequence from the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".