Corrigendum for Blonder <i>et al</i>. (2017) DOI: 10.1111/ele.12736
Bibliographic record
Abstract
First published 01 February 2017 Benjamin Blonder, Derek E. Moulton, Jessica Blois, Brian J. Enquist, Bente J. Graae, Marc Macias-Fauria, Brian McGill, Sandra Nogué, Alejandro Ordonez, Brody Sandel and Jens-Christian Svenning This article corrects: Predictability in community dynamics Volume 20, Issue 3, 293-306, Article first published online: 1 February 2017 Regarding the article ‘Predictability in community dynamics’ (Ecol. Lett. 20 (3), 293 –306), the authors would like to correct the error in the Figure 2 caption. In the description, it should read “coarsened and smoothed data (green curve) instead of “coarsened and smoothed data (blue curve)” The caption should read as follows: Figure 2 Definition of community response diagram statistics using an example data set. (a) A community's trajectory of observed climate F(t) and the community response C(t) is shown for original data (black curve), coarsened data (grey curve) and coarsened and smoothed data (green curve). The 1:1(no lag) expectation is shown as a diagonal red line. The maximum state number, n, indicates the largest number of unique values of C(t) that correspond to any coarsened value of F(t). It is calculated by intersecting a vertical line with the community's trajectory at all values of F(t) (vertical blue lines). (b) The mean absolute deviation, (Formula presented.) indicates the average difference between C(t) and F(t) across all times, with larger values indicating greater lags. The distribution of lags is shown as a grey envelope, and the statistic's value is shown as a vertical blue line. We apologize for any inconvenience caused.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.696 | 0.554 |
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".