Bibliographic record
Abstract
The prize honors the memory of David Cushing, Founding Editor of Journal of Plankton Research. It is awarded annually for the best paper by an early career stage scientist published in the journal during the previous year. The prize helps foster the interesting and high-quality papers by young scientists that David Cushing so actively supported. \n \nThe 2015 David Cushing Prize has been awarded to Bingzhang Chen for his paper, “Patterns of thermal limits of phytoplankton” (J. Plankton Res. 37, 285–292) \n \nBingzhang Chen obtained his PhD with a major in Marine Environmental Science at the Hong Kong University of Science and Technology under the supervision of Dr Hongbin Liu in 2008. He also worked with Dr Zoe Finkel and Dr Andrew Irwin in Mount Allison University, Canada, from 2009 to 2010. During this time, he started to learn the R language and entered the field of data analysis and programming. This experience has been proved very useful for his later work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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; both teacher heads agree on what is shown here.
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".