Claytonia : newsletter of the Arkansas Native Plant Society.
Bibliographic record
Abstract
Application (Continued on next page) (Continued from previous page)Making these legacy label data available is a core feature of the southeastern U.S. digitization project, and this article explains how Arkansas Native Plant Society members can become directly involved in this effort.An exciting aspect of the digitization project is the engagement of citizen science volunteers to assist in the legacy label data transcription from labels on plant specimen images to digital database fields.Volunteers can now work from anywhere with a computer and Internet connection to assist in label data digitization using the Notes from Nature web interface ( www.notesfromnature.org ) (Fig. 1). Call to ANPS Members to ParticipateWe need as many volunteers who are willing and able to transcribe specimen labels as we can muster.We only have functional database records for approximately 22,000 of the nearly 300,000 plant specimens in Arkansas.This means that as we generate the images, we may need to transcribe over 275,000 specimen labels for specimens within the state.Data transcription volunteers directly participate in science and help scientists assemble the datasets needed to answer today's important biological and conservation research questions.But what do the volunteers gain from participating?In my view, volunteers have the potential to gain a variety of benefits from participating in Notes from Nature transcriptions.By examining plant specimens, transcription volunteers can learn new plant species with which they were previously not familiar.They can become more familiar with broad geographic regions or specific locations within the state.Through transcribing label data, volunteers can gain insight into the history of state botanists and specific collectors.Moreover, they can gain a greater appreciation of the tremendous amount of work that went into buildingArkansas's natural heritage specimen records over the past century and a half.Finally, there is a strong sense of satisfaction that volunteers can gain by helping on a project that is so much larger than any one person, researcher, or institution.Arkansas plant specimen images and data are genuinely becoming a part of the global data infrastructure of natural history collections, and that is something of which to be proud.Summary I encourage all ANPS members to become engaged in a region-wide effort to digitize our state's natural history plant collections.These collections are used in a variety of research, teaching, and outreach applications, and we are calling on the volunteerism of citizen scientists to mobilize these data that have historically been inaccessible.Using the web-based platform Notes from Nature, specimen label data transcriptions are easy and interesting.I look forward to your participation in this project.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.483 | 0.256 |
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".