Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Meta-Data: The rep column is the number plots that I examined throughout my experiment. I survey one hundred 0.5mx0.5m quadrants in total. The date is when I collected my pilot experiment data for the solo survey, which was on October 20<sup>th</sup>, 2020. The researcher who conducted this experiment was me, James Zabbal. The location where I conducted this experiment was in a path/forest area that is located behind my house in King City, Ontario. It has a variety of different plant species there and is located beside a pond. The species richness column is how many different plant species were in each quadrant I had examined. The plants in my data <i>include Trifolium prantense, Symphyotrichum novae-angliae, Rudbeckia, Solidago canadensis </i>and<i> Sinapis arvensis</i>. The total cover column was a rough estimate (by percentage) of how much of each quadrant was covered by plants. Near pond is if the data I collected for my replicates was beside the lake or not. Y = yes = 0-5 meters distance from the pond. N = no = 30+ meters from the pond. I collected the data for my experiment by picking a start point (threw a rock in the air and where it landed is where I started) on the path and using a measure tape, I measured 0.5mx0.5m quadrants for each data point I collected. To include randomization in my experiment, I used a random number generator. For every even number, I took a meter step forward for my next data point, and for every odd number I took a 2-meter step forward for my next data point. I took notes of the different plant species I saw in each quadrant, and then roughly estimated how much of the quadrant space the plants took up (plants vs open land/grass).
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.
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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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 it