Agreed – there is no need to switch the modified Arrhenius function back to the old form
Bibliographic record
Abstract
Yin (2021) argues that the current implementation of the modified Arrhenius function (M2002; Medlyn et al., 2002), which omits a term that was included in the original derivation, should be retained over the corrected version (J1942; see Johnson et al., 1942; Murphy & Stinziano, 2021a). Yin further points out that there was a constant (1/4) omitted in a carbon balance modeling equation for electron transport limited CO2 assimilation, Wj. We formally acknowledge this unintentional error and apologize to our readers. We have since issued a Corrigendum on this (Murphy & Stinziano, 2021b), and advise readers that the Corrigendum needs to be considered before interpreting the original manuscript. The correction led to increased electron transport limitations on photosynthesis, which greatly reduced the magnitude of the impact of the missing Arrhenius term on daily carbon balance modeling to a consistently positive value between 0.02% and 0.6% per day that is markedly smaller than many other sources of uncertainty (Murphy & Stinziano, 2021b). There are other uncertainties in modeling carbon balance, in particular mesophyll and stomatal conductance (Rogers et al., 2017; Knauer et al., 2020), which likely introduce even greater uncertainty than the missing term in J1942. Therefore, from the perspective of research aiming to reduce uncertainty in modeling carbon uptake, there are higher priority subjects than switching the modified Arrhenius function back to its old form. For the data and code used in Murphy & Stinziano (2021a,b) see Stinziano & Murphy (2020; https://github.com/jstinzi/arrhenius.comparison).
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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 | 0.012 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.037 | 0.039 |
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".