Visions for insect conservation and diversity: spanning the gap between practice and theory
Bibliographic record
Abstract
First paragraph: Insect Conservation & Diversity has now successfully completed its first year in print, and we wish to extend our thanks to our Associate Editors, referees and contributors, as well as to our publishers for helping us achieve this milestone. We have all enjoyed the process of launching a new journal, although it has been, and continues to be, very hard work. We have published a variety of papers covering the general themes encompassed in the title of the journal and we hope that you, the readers and authors, have been engaged and intrigued by the papers we have presented to you. We also hope that you have been stimulated to submit your work to the journal. With that in mind, what would we, as editors of Insect Conservation and Diversity (ICD), like to read in the pages of ICD? Of course, top quality papers focusing on insect conservation and diversity and promoting the science of entomology at large are our brief. Needless to say, the topics to be tackled under this umbrella are many! Since it is our privilege to use a small space in this journal as editorial, we would like to take the opportunity to share with our readers our visions regarding what represents timely material for submission to ICD. These are of course, only examples and are not restrictive as to which material may be submitted to and printed in the pages of ICD!
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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 | 0.094 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.009 | 0.096 |
| Scholarly communication | 0.036 | 0.047 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".