“Taxonomic certification versus the scientific method”: a rebuttal of Rogers (2012)
Bibliographic record
Abstract
We read with great interest the correspondence article entitled “Taxonomic certification versus the scientific method” (Rogers 2012), and, as members of the Taxonomic Certification Committee of the Society for Freshwater Science (formerly, the North American Benthological Society [NABS, 1975–2011]) (SFS-TCC 2012), we agreed to respond in a constructive fashion with factual information to correct and provide perspective for a few errors, unfounded and confused assumptions, and misperceptions it presents. The nature and structure of the article and its title requires that our response be segregated into two main parts. First, we briefly describe the philosophy, purpose, and objectives of the Taxonomic Certification Program (TCP [http://www.sfstcp.com/]) as developed and administered by the SFS, including correcting inaccurate statements or false assumptions. Second, we will address the issues Rogers has with terminology used in a paper he cites (Stribling et al. 2003 [not 2002 as cited by Rogers]). The former issue is, by far, most important—primarily because it has the potential of adversely affecting a program that has already had a large positive impact on the quality of biological monitoring in the USA and Canada by recognizing laboratory staff with demonstrated ability to perform taxonomic identifications of benthic macroinvertebrate samples. The terminology issue is trivial, but because the comments are made in print, we correct them in print by rebutting Rogers’ perception that we were in error.
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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 | 0.114 | 0.285 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.080 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.049 | 0.086 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".