IISD Experimental Lakes Area: Chemistry of LTER Lakes, 1968-2024
Bibliographic record
Abstract
The IISD Experimental Lakes Area (IISD-ELA) Chemistry of LTER Lakes data package provides lake chemistry data from five reference lakes in northwestern Ontario, Canada for the years 1968-2024. This data package includes both tabular data and metadata files. The chemistry data includes the analysis of physical, nutrient, biological, inorganic, and organic parameters for long-term ecological research across lakes 114, 224, 239, 373, and 442. The dataset contains a row for each parameter measured at a specific date, lake, and depth (integrated layer or profile level). Metadata in this data package include a table of location coordinates and record counts and a PDF information sheet. The table of coordinates and counts is useful to know where the lakes and specific sampling sites are located and as an overview of data availability per lake (Lake 239 has the longest and most consistent data record). The info sheet provides additional metadata details about the dataset, including background and uses of the datasets, a data dictionary, diagrams, descriptions of methods and instruments, and additional references. The info sheet also contains a table that lists chemical parameter names and metadata used in this EDI repository alongside their equivalencies in the DataStream repository to help understand differences in naming conventions between the two parallel repository data packages. This data package is ongoing—updates will be provided as data are collected from these lakes in subsequent years. If data are not present here for a specific IISD-ELA lake that you are interested in, please get in touch with us. The data in this chemistry data package on EDI are also published in parallel at the Lake Winnipeg DataStream repository (https://doi.org/10.25976/fq3n-8207). The differences between the two are: the formatting of data and metadata (particularly how integrated sample information is organized across columns, and the differing chemical parameter names and metadata explained in the table in the info sheet), and the exclusion of the additional metadata files from DataStream (the metadata CSV and the info sheet PDF).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".