Time-series dataset of non-invasive IoT measurements in Apis mellifera colonies: fondant feeding during mild winters and early springs (2018–2020; Ukraine & Canada)
Bibliographic record
Abstract
The dataset contains time-series of non-invasive measurements of internal/external temperature (°C), relative humidity (%), and hive weight (kg) to support analysis of the thermal response of honey bee (Apis mellifera) colonies to fondant feeding under mild-winter and early-spring conditions. Locations and periods (UTC): Ukraine, Kyiv (2018-09-01 – 2019-04-30); Canada, Toronto (2020-02-01 – 2020-05-31). Measurements were taken in AmoHive hives (Langstroth, 10-frame) with a homogeneous sampling rate across parameters; timestamps are in UTC. Record contents: raw and clean tables (CSV + XLSX) organized by hive and period; an events/annotations file; a variable dictionary (data_dictionary_master.xlsx); method notes for cleaning and gap filling (methods_*.md); and a figure-reproduction script (generates two summary visualizations from the clean tables). Main variables: temp1 (internal temperature), temp2 (external temperature), humid1 (internal RH), humid2 (external RH), weight (units specified in the dictionary). See the variable dictionary for detailed descriptions. The dataset is intended for reproducible research on bee overwintering under mild-winter and early-spring conditions, anomaly analysis, and the development of predictive models. Data license: CC BY 4.0 (please cite the DOI of this record). Code license: MIT (for scripts included in the record).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".