Temperature, precipitation and soil characteristics of Volcan Cacao - Area de Conservacion Guanacaste (ACG), Costa Rica
Bibliographic record
Abstract
I have monitored temperature and precipitation at eight locations across a 1500m elevation gradient along Volcan Cacao in the Area de Conservacion Guanacaste (ACG) in northwestern Costa Rica since 2013 (January 2026 inclusive). This deposition includes air temperature data collected between March 2013 and September 2023. Air temperatures were recorded using either a HOBO RG3M Temperature and Rain Gauge Data Logger or an Onset HOBO UA-001-64 Pendant Temperature Data Logger or an Onset HOBO MX2202 Pendant Wireless Temperature Data Logger that recorded temperature every15 minutes each day. Datalogger is approximately 1.5 m above the ground. This deposition includes precipitation data collected between March 2013 and January 2026 as recorded using a HOBO RG3M Temperature and Rain Gauge Data Logger where each event logged was 0.2 ml. The deposition includes soil temperature data collected between June 2021 and January 2026. Temperature was recorded using an Onset HOBO MX2202 Pendant Wireless.Temperature Data Logger that recorded temperature every15 minutes each day. Datalogger is buried approximately 10-15 cm below the surface. This deposition includes measurements of soil chemistry as recorded in the field by an HH2 Moisture Meter Wet Sensor (Wet-2) (Delta T Devices, Cambridge, England) in February and August 2014. Inorganic elemental analysis was completed at the University of Guelph Laboratory Services by inductively coupled plasma optical emission spectrometry (ICP-OES) for: Calcium, Magnesium, Phosphorous, Potassium, Sodium, Sulphur, and Iron. A second analysis of elements in soil was conducted at SGS Argifood Laboratories (ug/g) by ICP using ammonium acetate extraction for P, K, Mg and Na.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".