How prevalent are pH‐specialist diatoms? A reply to Telford <i>et al.</i> (2006)
Bibliographic record
Abstract
Abstract Telford, Vandvik and Birks (2006) (henceforth TVB) refute our conclusion (Pither & Aarssen 2005) that only a minority of fossil diatom taxa within a diverse regional assemblage are pH specialists. They argue that the null model we used exhibited high Type II error rates for both geographically rare taxa (i.e. few lakes occupied) and for acid‐optima taxa, and assert that many more taxa would have been designated specialist had we adequate statistical power, and had more acidic lakes been sampled. However, TVB base their conclusions upon the results of additional analyses, which assume a priori that all taxa are pH specialists. Experimental and observational evidence do not support this assumption. As such, we contend that TVBs criticisms are unjustified.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".