Code Supplement for the artile: Exploring the spatially explicit predictions of the Maximum Entropy Theory of Ecology
Bibliographic record
Abstract
This file contains all the code needed to replicate the analyses of McGill, D.J., X. Xiao, J. Kitizes, and E.P. White. submitted. Exploring the spatially explicit predictions of the Maximum Entropy Theory of Ecology. http://biorxiv.org/content/early/2014/03/30/003657 This file should be decompressed in what R recognizes as the home directory (the R code will need to be modified if you decide not to use your home directory). R v 2.12 or higher and python 2.6 are needed to run the code. The following python packages are required: matplotlib, mpmath, numpy, and scipy, and the following R packages are required: vegan, hash, RCurl and bigmemory. It is also possible to access this code via GitHub at the following addresses: The primary code repository is located at: https://github.com/weecology/mete-spatial Additional scripts needed to run the core METE DDR functions are located here: https://github.com/weecology/METE https://github.com/weecology/macroecotools After the files are downloaded (from GitHub) or decompressed (from the mete-spatial.zip file) navigate to the METE directory and run the following python command python setup.py install run the same command in the macroecotools directory. To download the publically available data and run the analysis navigate to the directory ~/mete-spatial and run the command: Rscript ddr_run_all.R This script will download two datasets, analyze them, and graph the results. The plots will appear in the directory ~/mete-spatial/figs/
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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.031 | 0.000 |
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 teacher head, 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".