Comparative Brain Collections Are an Indispensable Resource for Evolutionary Neurobiology
Bibliographic record
Abstract
The underuse of these brain collections by neuroanatomists brought me to question why the collections are so often ignored or overlooked. At a practical level, many people are unaware that these collections exist, which is why the curators of these collections need to be more proactive. Researchers interested in broad comparative analyses also need to be encouraged to explore what is available in natural history museums and other specimen collections. Some museums, such as the National Museum of Natural History (Washington), have large numbers of brain specimens that could be used for neuroanatomical research. A second possible reason for the poor usage of brain collections is a concern raised by some authors regarding the compilation of neuroanatomical, specifically volumetric, data from a variety of sources. Roth et al. [2010] and Healy and Rowe [2007] have both criticized the use of data from disparate sources. The main reason underlying their criticism is simple: different methods result in different degrees of tissue shrinkage and thereby skewed volumetric measurements. This criticism is fair, but is it reasonable enough to exclude specimens under all circumstances? Should every researcher be expected to generate their own comparative brain colEarlier this year, Dr. John I. Johnson organized a conference in Washington, D.C., USA, as a tribute to the life and works of Wally Welker, a prominent member of the evolutionary neurobiology field [Johnson, 1993]. As part of this tribute, several people contributed talks focused on neuroanatomical collections throughout the world, including the Welker collection currently housed at the National Museum of Health and Medicine (Washington). A wide range of collections was discussed, some of which are well known (e.g. Heinz Stephan’s bat, insectivore and primate brain collection and Welker’s Wisconsin collection) and others not known at all to most scientists. What became clearly apparent to the conference attendees was that there are many brain collections worldwide and most of them are infrequently used. A potential consequence of this infrequent use is that the collections could be misplaced, destroyed or otherwise lost. Several people have since developed a committee (see http://braindatabases. wikispaces.com/) aimed at documenting these collections, increasing awareness of these collections within the scientific community and eventually digitizing these collections to make them more readily available for research and educational purposes. Published online: September 29, 2010
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.059 | 0.030 |
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".