Supplement 1. Data compiled for Snag_Stemwood and Snag_Branch annual fall rates from literature sources, with site and tree information, and methods used to compile values.
Bibliographic record
Abstract
File List Supplement_data.txt (md5: b4d1893fd59380fe7d7aacb04e8ab55b) Description Supplement_data.txt is a tab-separated file. It contains the data compiled from literature-sourced data, information about compilation methods, and sources. Column definitions: 1. RECORD_ID - record number. 2. SOURCE_ID - record number of reference used as data source (see list of references below). 3. SOURCE - short name for reference. 4. SITE - study site. 5. COUNTRY - country of study site. 6. REGION - region (province, state, other) of study site. 7. ECOZONE - Canadian ecozone if site in Canada or assigned to some sites in United States as the bordering Canadian ecozone. 8. SNAG_SP - tree species of snag 9. SPECIES_GROUP - species group category assigned to record based on the SNAG_SP or lead species of the study site. 10. OTHER_DESCRIPTOR - additional information about site or trees. 11. SNAG_FALL_ANNUAL - annual fall rate (%/yr) of standing dead tree stems meeting dbh criteria as Snag_Stemwood pool in CBM-CFS3 (Kurz et al. 2009). 12. SNAGBRANCH_FALLRATE - annual fall rate (%/yr) of standing dead material of non-merchantable trees, saplings, branches of merchantable-sized trees as in the SnagBranches pool of CBM-CFS3 (Kurz et al. 2009). 13. SNAGFALL_CALC_METHOD - brief description of data type used from literature source to calculate SNAG_FALL_ANNUAL, see methods in manuscript (Hilger et al. XXXX) for details of calculation methods based on T50 (mean half-life or time to 50% of snags fallen), individual % of stems fallen at a given time(s), or where values were derived from exponential k values. 14. SNAGBRANCH_FALLRATE_CALC - brief description of how data in reference was used to calculate SNAGBRANCH_FALLRATE.
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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.645 | 0.269 |
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".