Reply to Comment on ‘From the Paris Agreement to corporate climate commitments: evaluation of seven methods for setting “science-based” emission targets’
Bibliographic record
Abstract
Abstract The Science Based Targets initiative has published a Comment to our study (Bjørn et al 2021 Environ. Res. Lett. 16 054019). We see the Comment as an important step towards addressing our study’s call for more systematic presentation of methods for setting science-based targets and increased transparency behind the initiative’s method recommendations. We also agree with some of the Comment’s points of criticism of our study and the related nuances introduced. Yet, we find other points to be inaccurate or misdirected. Here, we reply to the Comment by clarifying misunderstandings on our study’s aims, providing additional methodological details, and elaborating on our perspectives.
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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 | 0.032 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.044 | 0.055 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".