Response to: ‘Meanderings into the Regulation of Effector Class by the Immune System: Derivation of the Trauma Model’ by Melvin Cohn
Bibliographic record
Abstract
Cohn M. Meanderings into the regulation of effector class by the immune system: derivation of the trauma model. Scand J Immunol 2012;76:77-88 delves into the discussion of how the immune system might regulate the decision between the immune response effector classes, and in particular identifies some key questions that need to be asked to understand how different classes of immune response occurring at the same time might be able to remain coherent and discrete. This is a needed discussion that advances the field, and the experiments proposed will go a long way to increasing our understanding of effector class regulation. However, in my opinion, the author makes some strong statements requiring substantiation regarding the impossibility of the involvement of germline-selected recognitive events as participating in self/non-self discrimination. Furthermore, the present discussion ignores a large body of contemporary literature describing the function and specificity of FoxP3+ regulatory T cells (T(reg) ) and formulates a theory that specifically excludes a role for T(reg) in maintaining self-tolerance without placing the contemporary evidence in the context of that theory. Thus in my opinion, these shortcomings should be addressed by the author.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.033 | 0.039 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".