Notes, Illustrations and Annotations on Sponge Specimens
Bibliographic record
Abstract
Size:44 .pdf files; a total of 22,107 pages, (30.2 GB in total) Scan details are located in the spreadsheet: 00b_HM_Reiswig_Specimen_Listing.xlsx Organization: Each .pdf file corresponds to one binder of notes by Dr. Henry M. Reiswig. The notes within the files and the .pdf files themselves are arranged chronologically, ranging from 1956-2019. Filenames include a unique number then a date range (e.g. 01_56-09-00_to_68-10-21.pdf ). The number refers to the original binder number and the date ranges are part of the specimen codes. Each specimen has a unique HMR specimen code, in the date format, YY-MM-DD.nn, where the date represents the day that Dr. Reiswig began examining the specimen. Every HMR specimen code is referenced in the spreadsheet: 00.b_HM Reiswig Specimen Listing.tab. For detail on how to cross-reference information to the files, see 00.a_Read_Me.txt. Content: Over 9,000 sponge specimens are presented in this dataset. The extent of the notes on each specimen varies from a single page of collection information to scientific sketches, scanning electron micrographs (SEMs), systematic notes, graphs, and Dr. Reiswig’s final taxonomic designation. About a quarter of the specimens were collected by Dr. Reiswig; the remainder were assessments requested from individual collectors, specimens loaned from institutions for examination and collections from institutions soliciting identification. This dataset includes specimens from about 60 institutions, museums and collectors. Most specimens represented herein are marine. The west coast of North America and the Caribbean are well-represented. Hexactinellid (glass sponge) specimens are common among the British Columbia (BC), Canada; New Zealand (NZ); and deep-sea locations.
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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.512 | 0.310 |
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".