Functional diversity of crustacean zooplankton communities: towards a trait‐based classification. <i>Freshwater Biology</i>, 52, 796–813. <scp>DOI</scp>: 10.1111/j.1365‐2427.2007.01733.x
Bibliographic record
Abstract
The superscripted numbers in Tables 1-4 in our original publication referred in some cases to the incorrectly associated reference number as listed in the Supplementary material. In this corrigendum, we have corrected the numbering scheme in Tables 1-4 and now refer to the corrected reference list in Table 5 (which replaces the Supplementary material file). These corrections do not alter the conclusions of the paper in any way. Additionally, there was an error in Table 4, in which the Calanoida column had been sorted by species name, but not the corresponding rows in the remaining columns of the Table. This resulted in incorrect functional traits listed for the Calanoida groups. Calanoida species are now listed with their correct functional traits in Table 4 of the corrigendum with associated references in Table 5. These corrections do not alter the conclusions of the paper because the correct traits were used in the calculation of the functional dendrograms. The authors apologise sincerely for the errors and any inconvenience these may have 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".