Data collection for Tsuji et al., 2020, Microbial ecology of phototrophs in Boreal Shield lakes, Chapter 3: Biogeography and activity of chlorophototrophs in the ferruginous water columns of Boreal Shield lakes (PhD thesis)
Bibliographic record
Abstract
This data collection includes supplementary or raw data files related to Chapter 3 of the PhD thesis of Jackson M. Tsuji, "Biogeography and activity of chlorophototrophs in the ferruginous water columns of Boreal Shield lakes" (in "Microbial ecology of phototrophs in Boreal Shield lakes"). Specifically, the following files are included: ASV_table_non_rarefied_counts.tsv.gz -- non-rarefied ASV table containing 16S rRNA gene amplicon data presented in this study as raw counts. Beyond the index column and sample columns, two additional columns, "Consensus.Lineage" and "Sequence" are included in the table. These columns include the taxonomic classification of the ASV (according to Silva) and the ASV sequence, respectively. ASV_table_non_rarefied_percent.tsv.gz -- same as above, but the data are normalized within each sample and expressed as percentages (i.e., sum to 100%). ASV_table_rarefied_counts.tsv.gz -- same as "ASV_table_non_rarefied_counts.tsv.gz", except that data is rarefied to 12,000 sequences per sample. Five samples were dropped due to having <12,000 sequences. ASV_table_rarefied_percent.tsv.gz -- same as above, but the data are normalized within each sample and expressed as percentages (i.e., sum to 100%). MAG_abundances_to_unassembled_reads.tsv.gz -- table like an ASV table showing the relative abundances (expressed as percentages) of metagenome-assembled genomes within metagenomes. Aside from the index column and sample columns, additional columns are included to provide the taxonomic classification of the MAGs (based on the Genome Taxonomy Database) and the CheckM statistics of the MAGs. Relative abundances of MAGs in a metagenome are calculated as the number of mapped reads to the MAGs from the given metagenome divided by the total number of unassembled metagenome reads for that metagenome (times 100%). MAG_abundances_to_assembled_reads.tsv.gz -- same as above, except that relative abundances are divided by the total number of unassembled metagenome reads for that metagenome that mapped to that metagenome's assembled contigs. core_sample_metadata.tsv -- table of core physico-chemical and geographic metadata for the samples in this study (used to build biplots presented in the chapter). Note that "nd" means "no data available", and any measurements below detection limits have been set to 0. A limited number of values were inferred from other sampling time points -- these are noted in the table for TDFe measurements, and in addition, the light attenuation coefficient for Lake 373 in Sept. 2017 was inferred from the Sept. 2016 coefficient due to no light data being available for Sept. 2017 samples. metadata_descriptions.tsv -- descriptions of all metadata columns in the above file.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.138 | 0.068 |
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".